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Ransomware deployment methods and analysis: views from a predictive model and human responses


Ransomware incidents have increased dramatically in the past few years. The number of ransomware variants is also increasing, which means signature and heuristic-based detection techniques are becoming harder to achieve, due to the ever changing pattern of ransomware attack vectors. Therefore, in order to combat ransomware, we need a better understanding on how ransomware is being deployed, its characteristics, as well as how potential victims may react to ransomware incidents. This paper aims to address this challenge by carrying out an investigation on 18 families of ransomware, leading to a model for categorising ransomware behavioural characteristics, which can then be used to improve detection and handling of ransomware incidents. The categorisation was done in respect to the stages of ransomware deployment methods with a predictive model we developed called Randep. The stages are fingerprint, propagate, communicate, map, encrypt, lock, delete and threaten. Analysing the samples gathered for the predictive model provided an insight into the stages and timeline of ransomware execution. Furthermore, we carried out a study on how potential victims (individuals, as well as IT support staff at universities and SMEs) detect that ransomware was being deployed on their machine, what steps they took to investigate the incident, and how they responded to the attack. Both quantitative and qualitative data were collected through questionnaires and in-depth interviews. The results shed an interesting light into the most common attack methods, the most targeted operating systems and the infection symptoms, as well as recommended defence mechanisms. This information can be used in the future to create behavioural patterns for improved ransomware detection and response.


Ransomware is a form of malware that blackmails its victim. The name “ransomware” comes from the ransom note asking its victim to pay some money (ransom) in return for gaining back access to their data or device, or for the attacker not to divulge the victim’s embarrassing or compromising information. It usually spreads through malicious e-mail attachments, infected software apps, infected external storage devices or compromised websites. Unlike other types of malware (which typically try to remain undetected), ransomware exposes itself at some stage of its execution in order to deliver the ransom demand to its victim. This demand is usually presented with a note that appears on the screen before or after the encryption occurs, outlining the threat and accompanied by a detailed set of instructions for making the payment, typically through a cryptocurrency.

Ransomware has had a rapid year-on-year growth of new families since 2013, costing an estimated more than 5 billion USD globally and growing over an expected rate of 350% in 2017 (Morgan 2017; Clay 2016). The majority of ransomware strains target Windows operating systems (Mansfield-Devine 2016) and are of the crypto-ransomware type (Savage et al. 2015). Crypto-ransomware attacks have a greater threat than any other type of ransomware, as they can lock out a user from valuable assets, affecting productivity and availability of services. The attacks mainly affect small and medium sized enterprises (SMEs) (Savage et al. 2015) and critical infrastructure including educational institutions and healthcare trusts (Barker 2017; Dunn 2017; Heather 2017), which are more likely to fall victim or flounder under the pressure and pay to release the encrypted contents. The number of attacks has grown partly because malware authors have adopted an easy-to-use modular design of the ransomware. Furthermore, Ransomware-as-a-Service (RaaS) products (Conner 2017; Cimpanu 2017) have become more readily available, which assist the attacker through simplistic distribution with phishing and exploitation kits and a trustworthy business model.

The attacks are often achieved through leveraging social engineering tactics to get a victim to download and activate the binary, which evades the anti-virus scanner’s signature-based detection through oligomorphic or polymorphic decryptors, metamorphic code (Szor 2005) or the generation of a new variant. According to Symantec’s reports (Savage et al. 2015; O’Brien et al. 2016), phishing attacks are the prime cause of ransomware being activated on a victim’s computer. A likely scenario of the vectors toward activation could be from an email with a payload or a link to a website that triggers a drive-by-download. The downloaded binary could initiate the process of carrying out the ransom, or in cases of more sophisticated attacks, it will first fingerprint the victim’s environment prior to dropping the malicious binary or process (Lindorfer et al. 2011).

Researchers have analysed ransomware variants, but are yet to propose a predictive model of ransomware deployment methods. It is vital to have a deep understanding of the deployment methods of ransomware to effectively fight against them.

The main contribution of this paper is a predictive model of ransomware stages, which came out from a study of 18 ransomware families by looking into Windows Application Programming Interface (API) function calls during each ransomware execution. Another contribution of this research focuses on querying and interviewing ransomware victims to find common factors between attacks, in order to be able to generate a more high-level understanding of ransomware deployment methods.

The rest of the paper is organised as follows. The "Ransomware overview" section provides a more in-depth look into ransomware, including its attack vectors, the way it may target user files, as well as an outline of related work, both in understanding ransomware and in combatting it. The "Methodology" section outlines the two-pronged methodology used in our research, namely the development of a predictive model of ransomware deployment, and the user study to gain better understanding on ransomware deployment. The "Results, analysis and discussion" section presents the results of our research, in particular the predictive model of ransomware deployment involving the stages of ransomware deployment, leading to ideas for preventive action to deal with ransomware deployment threat effectively. The results from the user study are also summarised, analysed and discussed, shedding light into the ransomware victims’ perception and behaviour in the aftermath of a ransomware incident. All of these may contribute towards better techniques in combatting ransomware. "Conclusion" section concludes our paper and presents some ideas for future work.

Ransomware overview

In 1996, Young and Yung introduced the idea of cryptovirology (Young and Yung 1996), which shows that cryptography can be used for offensive purposes, such as extortion. Since then, this idea had evolved into ransomware, and ransomware has become a growing cyber security threat, with an increased number of infections and many variants being created daily. According to a Symantec report, 98 new ransomware families were found in 2016, more than tripling the figure for the previous year (Symantec: Internet Security Threat Report 2017).

The main types of ransomware are scare, lock, crypto, and wipe, where the latter was first seen with the 2017 PetrWrap attack that encrypted the Master File Table (MFT) of victims, but did not unlock it after payment. Encrypting the MFT renders the content of a hard drive unusable, and is rarely used among ransomware families. Other examples of crypto-ransomware targeting the MFT include Seftad (Kharraz et al. 2015), Petya (Mansfield-Devine 2016), and Satana (Villanueva 2016). The latter two (as well as PetrWrap) start by corrupting the MFT and forcing the operating system (OS) to reboot. Like computer worms (Szor 2005; Yang et al. 2008), ransomware can self-propagate such as when TeslaCrypt infected a laptop integral to a gambling website and led to spreading itself to over 15 servers and 80 other connected computers through the use of shared folders (Spring 2016). Perhaps the most infamous ransomware is the WannaCry cryptoworm, which hit the headline in May 2017, and affected more than 200,000 computers in 150 countries, including the UK National Health Service (National Audit Office 2017).

Attack vectors for distributing ransomware

Various tactics are used by ransomware attackers to get their victims to activate the malware, grant it elevated privileges, and submit to the demands. Common infection vectors of ransomware include phishing, exploit kits, downloader and trojan botnets, social engineering tactics, and traffic distribution systems (Sgandurra et al. 2016). Despite phishing still prevailing as the preferred choice for deployment (Savage et al. 2015), in 2015–2016 there was a noticeable increase in the use of exploit kits, such as Angler, which was used to spread CryptoWall and TeslaCrypt in 2015 (Abrams 2016a). Angler had a very high activity in the malware distribution world until the arrest of its developers in 2016 (Cisco 2017).

Due to the nature of the attacks, ransomware can be seen as having a business model (Hernandez-Castro et al. 2017), where victims are the attackers’ customers who purchase decryptors or keys to regain access to assets. Hence, attackers should be in the mindset of taking advantage of the victim without them noticing until presented with the ransom note. The note should deliver a clear message that provokes or threatens the victim to pay, and should have user-friendly and reliable methods for the victims to follow in order to pay and regain access (Andronio et al. 2015). Moreover, due to the international scale of the ransomware market, ransom notes need flexibility in language based on the target’s locale.

The business model breaks when either the integrity of the crypto-virus’ encryption is broken, payment transactions are denied or unsuccessful, or the encrypted files become unavailable to the decryptor. For the sake of maintaining ransomware’s reputation of returning access after payment, ransomware authors develop their code in a modular fashion to enable simple generation of variants by less-skilled coders or even script-kiddies (Mansfield-Devine 2016; Sinitsyn 2015). Moreover, the development of Ransomware-as-a-Service (Cimpanu 2017), has further simplified the process for aspiring ransomware attackers, while maintaining the quality of attacks.

Since 2013, ransomware has increasingly integrated fingerprinting measures to get the time, date, language, and geolocation (Savage et al. 2015) to facilitate social engineering on a global scale with ransom notes presented in the victim’s language. For instance, some ransomware identifies the locality and language of the targeted computer and hence displays the note in that language. The least costly ransom note is text-based, however, other delivery mechanisms have been used including recorded voice. Examples of language-sensitive ransomware include Reveton, with 10 translations of a text-based ransom note and the March 2016 version of Cerber, which has 12 recorded voice ransom notes in the 12 most common languages (Clay 2016).

How ransomware targets user files

The signature characteristics of how ransomware targets user files is through mapping the user environment. Targeted files need to be recent and of some value or importance, therefore ransomware may look at the recent files history and usually maps important folders, such as My Documents, Pictures, and other generic folders, as well as the Recycle Bin (Abrams 2016a, b; Lee et al. 2017). Whilst mapping, a process counts the number of mapped files, based on the extension and their location, and reports the results to the Command & Control (C&C) server (Hasherezade 2016). To determine the importance of the files, the last accessed date is observed, and a difference is calculated between the creation and last modified date, both of these indicate the amount of work carried out on a file, as well as the user’s level of interest (Kharraz et al. 2015). To ensure the files are genuine, the ransomware calculates the entropy, which is the information density, of the file names and their contents (Kharraz et al. 2016). If the entropy is too high or low, resembling random content or just padding respectively, the ransomware will interpret the file as auto-generated, and discard it from its map. After mapping, it will either request from the C&C to start encryption along with the number of files targeted, or instantly start encrypting (Hasherezade 2016; Kharraz et al. 2016).

The ransom message may take the form of an application, Blue Screen of Death, a text file on the desktop, screen-saver or other means of gaining the user’s attention. The encryption phase has varying levels of robustness, from the trivial coding of base64 to Advanced Encryption Standard (AES), where the most common form is AES-256 for symmetric encryption (Savage et al. 2015; Mansfield-Devine 2016). Additionally, the names of the files will frequently be changed to signify locking, often adding an extension related to the ransomware family name.

Related work

Many researchers (Andronio et al. 2015; Lee et al. 2016; Kharraz et al. 2016; Sgandurra et al. 2016; Zscaler 2016) agree that crypto-ransomware’s typical behaviour involves the manipulation of files and displaying a threatening message, which can be identified through the ransomware’s use of Windows API function calls. It is possible to monitor read, encrypt, and delete operations called at the user-level, which are then passed onto the kernel to the input/output (I/O) scheduler (Kharraz et al. 2016). According to (Kharraz et al. 2016) there are three ways ransomware encrypts files: (i) overwriting originals with the encrypted versions, (ii) encryption then unlinking of the originals, and (iii) encryption and secure deletion of the originals.

Behavioural heuristic detection through the mapping of Windows API function calls can be useful for detecting potential ransomware attacks, but it may suffer from high false positive rates (for example, the legitimate owner of the files may choose to encrypt their files, which would exhibit ransomware-like behaviour). Therefore, it is important to complement the behavioural heuristic approach with techniques based on deployment characteristics of ransomware, including possible classification to ransomware families. This will enable more subtle and more accurate behavioural analysis—such as a typical sequence of actions and timing of Windows API function calls, as well as other behavioural patterns – to be considered before deciding whether a particular set of activities have a high probability of indicating a ransomware attack, or even, it represents known behaviour of a particular ransomware family. As ransomware families may evolve (e.g. by changing the function calls used), it is important to still be able to detect potentially malicious behaviour of the new variants. Our contribution is through modelling the higher-level behaviour of the sample and analysing them to determine if they represent a potential ransomware deployment taking place.

Tools and strategies for analysing ransomware

The development and use of sandboxes in the security industry has enabled a secure environment for the activation and analysis of malicious samples. Monitoring tools are integrated into sandboxes to observe and report on the sample’s behaviour at the user and kernel-level. Malware analysis is available online at, and, as a bare-metal sandbox such as Barecloud and BareBox (Yokoyama et al. 2016), and as a package such as RanSim (KnowBe4 2017), REMnux (Zeltser 2014), Cisco (Umbrella 2016; Zscaler 2016; SonicWall 2016) and the well-known Cuckoo Sandbox (Ferrand 2015; Yokoyama et al. 2016; Kharraz et al. 2016). Cuckoo Sandbox allows the submission of Dynamic Linked Libraries (DDLs), Java files, binary executables, URLs, MS Office documents, and PDFs as samples (Ferrand 2015). Several researchers have developed analysis systems for the detection and classification of ransomware threats including Unveil (Kharraz et al. 2016), HelDroid (Andronio et al. 2015), EldeRan (Sgandurra et al. 2016), and CloudRPS (Lee et al. 2016).

Kharraz et al. (2016) developed a ransomware detection and classification system called Unveil that identifies ransomware based on its behavioural constructs. Unveil is fully automated, and works with Cuckoo Sandbox, where they submitted hundreds of thousands of malware samples into Windows XP SP3 virtual machines. The analysis returned a high percentage of successful detections of samples of known ransomware. The author’s approach is through monitoring access patterns of the sandbox’s filesystem at the kernel-level, as well as pattern matching of text in the ransom note for threatening phrases.

Sgandurra et al. (2016) developed an automated program for the dynamic analysis of ransomware, called EldeRan, which uses machine learning to classify malicious samples based on their early behaviour. They have mapped key behavioural features to enable the detection of new variants and families. The program needs a few behavioural characteristics for training, for which they used Regularised Logistic Regression classifiers. The outcome is a detection system that has less than 6% error-rate, and above an average of 93% at detecting new ransomware families.

EldeRan (Sgandurra et al. 2016) works with Cuckoo Sandbox, machine learning and negative feedback to determine a set of key features for ransomware. Training data, consisting of benign software and malware, are dynamically analysed based on five attributes: API invocations, use of registry keys, file or directory operations, Internet download activity, and hardcoded strings. EldeRan was trained in Windows XP SP3 32-bit, which is more vulnerable than later editions of the Windows OS suite. However, since the OS has been deprecated since 2014, it would have been beneficial to test or train a version on Windows 7 or later. This would have given a good comparison of how well the system works over different generations.

Identification of ransomware families is indeed a valuable research angle, as demonstrated by several other papers. Homayoun et al. (2017) used Sequential Pattern Mining to detect best features that can be used to distinguish ransomware applications from benign applications. They focussed on three ransomware families (Locky, Cerber and TeslaCrypt) and were able to identify a given ransomware family with a 96.5% accuracy within 10 s of the ransomware’s execution.

CloudRPS (Lee et al. 2016) is a cloud-based ransomware analysis system, which supervises an organisation’s activity over the internet. Based on behavioural analytics, it quarantines and classifies suspicious downloads, which are analysed dynamically in a sandbox.

Andronio et al. (2015) developed HelDroid, which analyses and detects ransomware on Android devices, where the system monitors actions involving locking, encryption, or displaying a ransom note. The detection of threatening text uses optical character recognition and natural language processing to facilitate detection in potentially any language. Like Unveil, HelDroid monitors the ransomware’s access to system APIs for locking, encryption, network activity, file renaming and deletion.

Another promising approach for detecting the presence of ransomware (and malware in general) is by monitoring the energy consumption profile of the device. This approach could be more robust compared to other detection techniques based on the behaviour or pattern profile of the device, since it is harder to hide or fake energy consumption characteristic. A paper by Azmoodeh et al. (2017) demonstrated the feasibility of this energy consumption monitoring approach for detecting potential ransomware apps on Android devices. They managed to achieve a detection rate of 95.65% and a precision rate of 89.19%, which point to the feasibility of this approach.

Tools for combatting ransomware

There are also tools that can be used to protect against ransomware, for example by early detection of ransomware attacks in progress and/or through recovery measures to neutralise the need to pay the demand. These tools are valuable and complementary to the work we present in this paper. Several of these tools are described below for completeness but they are not discussed further in this paper.

PayBreak (Kolodenker et al. 2017) took a proactive approach in combatting ransomware by implementing a key escrow mechanism in which hooks are inserted into known cryptographic functions such that the relevant encryption information (the symmetric keys) can be extracted. This approach came about from an insight that efficient ransomware encryption needs a hybrid encryption in which symmetric session keys are stored on the victim’s computer (in particular, their key vault, which is secured with asymmetric encryption allowing the victim to unlock the vault using their private key). After the victim’s computer is infected with ransomware, they can access their vault and PayBreak attempts to decrypt the encrypted files using the symmetric session keys stored in the vault, therefore saving the victim from paying the ransom.

Another approach to recover from a ransomware attack without needing to pay a ransom is by copying a file when it is being modified, storing the copy in a protected area and allowing any changes to be made to the original file. This approach is used by ShieldFS (Continella et al. 2016), which keeps track of changes made to files. When a new process requests to write or delete a file, a copy is created and stored in a protected (i.e. read-only) area. If ShieldFS decides later that this process is benign, the copied file can be removed from the protected area as the assumption here is that the original file has not been encrypted by ransomware. However, if ShieldFS determines that a process is malicious, the offending process will be suspended and the copies can be restored, replacing the modified (encrypted) versions.

Redemption (Kharraz and Kirda 2017) uses a similar approach to ShieldFS, but in Redemption, file operations are being redirected to a dummy copy. This technique creates a copy of each of the files targeted by the ransomware, and then redirects the filesystem operations (invoked by the ransomware to encrypt the target files) to the copies, hence leaving the original files intact. Redemption uses the Windows Kernel Development framework to redirect (“reflect”) the write requests from the target files to the copied files in a transparent data buffer.


We developed a predictive model of ransomware, in our attempt to characterise all variants of each family of ransomware into one model. The process included the development of a classifier (to parse, classify and output graphs detailing the behavioural constructs of a ransomware), as well as creating a safe environment to analyse the ransomware samples.

In conjunction to this model, we carried out a user study to get a picture of ransomware deployment process.

Ransomware deployment predictive model

Designing a model to predict deployment characteristics of all ransomware families is not a trivial task, because different malware authors are likely to develop their code base differently. Furthermore, there is a high chance of code evolution and adaptation over time, as some ransomware source code may be made available and shared among malware authors. However, there are likely some similarities among ransomware families in the flow between the stages of execution.

The 18 ransomware families investigated in this research are Cerber, Chimera, CTB-Locker, Donald Trump, Jigsaw, Petya, Reveton, Satana, TeslaCrypt, TorrentLocker, WannaCry, CryptoLocker, Odin, Shade, Locky, Spora, CryptorBit, and CryptoWall. These were chosen based on their threat-level, amount of infections, originality and media coverage. The details about three influential ransomware samples (TeslaCrypt, Cerber and WannaCry) are provided in "Mapping ransomware variants to the Randep model" section.

We looked at the Windows Application Programming Interface (API) function calls made by these ransomware families, in order to understand what activities a ransomware strain might do, and what stages it might get into. There are thousands of Windows API functions, and each sample analysed would use hundreds of those multiple times, making classification of functions into our ransomware deployment model a laborious process. Hence, we made a collection of all functions used by samples and reduce them into a list for classification into the model. To enable the plugging in of functions into the model, the category and description are gathered from Microsoft’s web site to decrease the load of the classification process; either manually or automatically through an API scraper developed in our research. As a result of this exercise, we developed a model called Randep, being an amalgamation of ransomware and deployment. The Randep model contains eight stages that pair with matching function calls.

Fig. 1
figure 1

Flow chart of Randep classifier with steps through the parser, categoriser, classifier, mapper according to the Randep model, and output of results as a graph

Development of Randep classifier

Cuckoo generates JSON reports for each sample analysed, detailing Windows API function calls, network traffic, loaded libraries, registration keys, and file I/O operations. Figure 1 shows a flow chart of the Randep classifier, which classifies Cuckoo reports into Randep graphs. Five of the six main processes (parser, categorise, classify, Randep map, and plot) are handled by the Randep classifier, which calls the remaining process (web scraper), as a subprocess. Since the size of a typical Cuckoo report sits in hundreds of MBs, processing each one on every invocation of the classifier would be costly. Hence, the results are permanently stored as JSON files at the end of each process to decrease RAM cost, and to extract key information about the binary. The Randep classifier is available online with examples from

Classification of Windows API functions into the Randep model

The Randep classifier’s parser maps Windows API functions, signatures, registration keys, and network calls into categories of the eight states defined by the probabilistic Randep model. The classification of functions into the states of the Randep model can be carried out manually or with the use of machine learning. We considered the use of machine learning as future work, but it is out of the scope of this paper. The work of manual classification has been reduced through the categorisation of functions and the API scraper’s gathering of descriptions and Microsoft API web page links. The results were combined using a Python script called, which outputs any conflicts of functions in different states. Those that had a conflict were discussed between the team members until an agreement was reached on the appropriate class for a particular function.

The classification of the Windows API functions into the Randep model serves as a template or skeleton for the Randep classifier to map a ransomware sample’s function calls into states. However, further adjustments to the model should be made in cases where a particular function fails to sufficiently define its state within to the Randep model.

Sandbox hardening

Sandbox hardening involves denying any malicious activity from leaking between privilege rings, or out from the virtual machine (VM) container, as well as ensuring the analysis system is not detected, and that the sample will activate. As a simple precautionary measure, stealth malware is known to sleep or use stalling code to prevent detection while under surveillance in a sandbox (Sikorski and Honig 2012). However, most malware authors intend to promptly unleash the payload to avoid failure through a user restarting the machine or being detected by anti-virus software (Kharraz et al. 2016). Developments of hypervisors including VMware and Oracle’s VirtualBox have been tested and improved for flaws where an attacker can escape into the physical machine or affect the bare metal (Balazs 2016; Duckett 2017). A well-known and secure sandbox, Cuckoo SandboxFootnote 1 has been developed with security in mind, however; some malware is known to detect the analysis environment, and security analysts should take actions to defend against such vulnerabilities (Ferrand 2015).

It is crucial to harden the system to prevent leakage from guest to host. We used a tool called Pafish (Paranoid FishFootnote 2), which allows security researchers to develop VMs with anti-fingerprinting strategies. To decrease the number of flags generated by Pafish and harden the sandbox VM, we copied the system information from a bare-metal machine into the VM’s configuration, allocated 2-CPUs, 4 GB RAM, 256 GB HDD in VirtualBox, and used from

The user environment was populated with programs, files and folders automatically using VMCloak and the antivmdetection script. The antivmdetection script required a list of filenames, which can be automatically generated using a random word generator at, as well as a range of size for the files. Injecting the script to run on each submission of a sample will avoid the VM from being fingerprinted based on information of the files and folders. Using VMCloak we installed programs including Adobe Reader 9.0, Google Chrome, MS Office 2007, and Java 7 (some of these are old or legacy software, but they are still often found in potential target machines, hence their inclusion in the VM configuration).

User study methodology

As part of our research, we also wanted to ask the general public about their experiences with ransomware attacks to get a picture of how ransomware gets deployed. To get this information, we developed questionnaires, with the main target groups being students, SMEs in the UK, as well as universities in the UK and in the US.

We wanted a clear, manageable scope, but also aimed to find a high number of victims for the best possible result. Being hit by ransomware can be a sensitive subject to many organisations, hence the scope had to be decided carefully. Being part of a university research project, we wanted to learn from other students and universities. Students are typically active online, with limited knowledge of the threats. While getting information from them, we also wanted to spread awareness of ransomware attacks. The expectation was that universities and students would be more open to participate in a study conducted by other students, while at the same time, being the likely targets.

To widen the scope for more hits, we decided to include SMEs. SMEs are also potential targets for ransomware attacks, and they are often seen as an easy target by the attacker, due to the likelihood that they do not have a dedicated security team, or the relaxed atmosphere in their operation (NCSC and NCA 2018).

We gave questionnaire respondents an opportunity to participate in a follow-up interview to gain further insight into the attack, as well as a better understanding of the respondents’ views on ransomware.

Questionnaire generation

Three separate questionnaires were created, one for each target group (students, SMEs and universities). The questions were mostly the same, but small alterations were made considering the technical orientation of the respondent group. Forming the questions, the assumption was made that all participants for the student questionnaire were in higher education in the UK or in the US, and meeting the minimum university-level English language requirements. Additionally, the student questionnaire questions assumed that the respondents were not technically oriented. The university and SME questionnaires were formed with the assumption that the respondents were working in the IT sector with a higher level of technical understanding. Notwithstanding, this limitation was taken into consideration that respondents may perceive questions in different manners and have different backgrounds.

Respondents were asked to give their consent before proceeding. If the respondent indicated that they had not been previously infected by ransomware, the questionnaire would end, otherwise questions related to when and how the infection happened and what operating systems were involved would be asked. Based on their answers, further questions were presented and some sections skipped. The final part was always the same, and included further details about the attack, such as how many devices were infected and whether data could be recovered.

Questionnaire distribution

We carried out the initial student questionnaire at our University. To reach the students, the communication officers at each School were contacted, asking them to help by posting the questionnaire in different newsletters and blogs around the University. The questionnaire was also posted on several social media sites. The student questionnaire was sent out in March 2017.

The strategy with the Universities was to gather contact details for the IT department of each University and contact them asking whether they would be willing to participate in our research. Only if they agreed, the link to the online questionnaire was provided. This strategy was used because an email coming from an unknown source can be seen even more suspicious if it includes a link. Universities in the UK were contacted in April–May 2017, and universities in the US in June–July 2017.

SME contact details were gathered from company websites. A similar strategy to the one with the Universities was used, where first their willingness to participate was enquired. The SMEs were contacted in June–July 2017.


The questionnaire was kept completely anonymous. However, at the end of the questionnaire, the respondents were given an opportunity to provide their email address and volunteer for an additional interview. Eight respondents volunteered to proceed to the in-depth interview.

The interviews were conducted via Skype, phone or email, depending on the respondent’s preference. The questions mainly focused on getting further details of the most recent attack they talked about in the questionnaire, but also on getting information about their planned and/or implemented defence measures against ransomware attacks. The interview questions were similar in each interview, but were altered based on the responses the participants had given in the questionnaire. During each interview, the discussion was audio-recorded with the permission of the interviewee. Afterwards, the audio data were typed for record keeping and qualitative analysis.

Results, analysis and discussion

This section presents the results and analysis of applying the Randep model on 18 families of ransomware, along with the results and analysis of the user study. Each part is accompanied by relevant discussion to explain the findings and insights gained from the research.

Model of predictive nature of ransomware

If we look at the higher level, ransomware (in particular, crypto-ransomware) will likely have three stages: stealth (in which its main priority is to remain undetected while it prepares the groundwork for the ransomware attack), suspicious (in which it starts carrying out the damaging part of the attack, but it may not be detected straight away), and obvious (in which it makes its presence known to its victim, namely by notifying of its demand through a threatening message, and by deleting the victim’s files). The transition at the higher level is pretty straightforward: stealth, followed by suspicious and then finally obvious.

Looking deeper, there are several lower level stages that ransomware may exhibit. These are probabilistic in nature, in a sense that not all ransomware strains will have all of these stages and/or the transition sequence between stages may differ. The lower level stages are:

  • Fingerprint creating signatures of the OS’s features and determining suitability for payload deployment.

  • Propagate exploring the possibility of lateral movement within a network or connected devices.

  • Communicate sending and receiving data from the attacker’s C&C server.

  • Map reading the contents of suitable files in the victim’s environment.

  • Encrypt encrypting potentially valuable data on the victim’s computer.

  • Lock reducing or disabling the availability of the OS to the victim.

  • Delete overwriting or unlinking the contents of the victim’s data.

  • Threaten presenting a threatening message to force the victim to pay up.

Figure 2 depicts our Randep predictive deployment model of ransomware. We have also developed a Randep classifier, which maps the Window API function calls, signatures, registration keys, and network calls into categories of the eight stages outlined above.

Fig. 2
figure 2

Predictive model of ransomware deployment methods

Lock-type ransomware would at least employ lock and threat stages. The majority of new ransomware families (> 95% in 2016) are of the crypto variety, therefore it is worth to focus on the actions of this type of ransomware. Crypto-ransomware has at least three stages: generating a map of files to encrypt, encrypting them, and displaying a threat. We consider the mapping activities to be a stealthy operation, since it would not alter the user experience, whereas the encryption activities are suspicious, as they will involve a “write” operation to create a new file, and the threat is obvious to the user, as it should spawn a window to cover the majority of the desktop to draw the user’s attention.

Each analysed ransomware sample behaved differently in terms of Windows API function calls. Some started encrypting immediately after entering the device and others spent more time on communicating, mapping, fingerprinting and/or propagating. However, there were some function calls that appeared in multiple results. SetFilePointer could be seen as a part of many encryption processes, as well as CryptEncrypt. Most samples did some mapping or fingerprinting by enquiring system info by calling functions such as GetSystemTimeAsFileTime. Functions NtTerminateProcess and LoadStringW were also called by many samples, the former can be seen to represent the locking stage and the latter the threatening stage (displaying the ransom note).

The first functions called by the samples (prior to encryption) are the ones that could be used for ransomware detection. For example, in the case of Cerber, the main encryption phase starts only after 330 s. Also types like WannaCry and TeslaCrypt spend more time fingerprinting and profiling their target. During this time, there is a chance to stop the execution before the real damage is done. Ransomware types that begin encryption immediately (e.g. CryptoLocker and Satana) are more challenging to stop. Possibly, if the plug is pulled immediately after the device is infected, at least some files could be saved. In other cases, such as Jigsaw, the ransom note is displayed before encryption starts, meaning the encryption phase could possibly be stopped by shutting down the device as soon as the ransom message is seen. The function calls can be used for ransomware detection in automated future solutions.

Randep model case distinction

The Randep model has two levels of stages: the higher level denotes stealth, suspicious, and obvious, and each contain other finite stages at a lower level. Since each lower level stage can be processed in parallel, it is not straightforward to determine which process starts and ends first. So instead, we look at any edges between stages measured in terms of a control flow diagram, propagation time, mutual parameters, CPU threads, callbacks, and other processes. Our research has developed potential links for each stage at both higher and lower levels, as shown in Fig. 3. The links between stages represent two hypotheses between the two connected entities, where the direction is indicated by the order of letters in the subscript, e.g. \(H_{FC}\) is a hypothesis that F (Fingerprint stage) is followed by C (Communicate to C&C stage), as opposed to \(H_{CF}\), in which C is followed by F.

Fig. 3
figure 3

Potential links between stages at lower and higher levels

At the higher level of the Randep predictive model, we hypothesise a flow from stealth to suspicious to obvious; \(H_{StSu} \Rightarrow H_{SuO}\). Stealth is first due to ransomware needing to scope out a suitable environment for deployment, to avoid detection by anti-virus vendors, and to appear as normal to the victim. Suspicious activity acts second, as the ransomware needs to hook its process and access the required privilege level to carry out malicious behaviour, which might seem suspicious to some vigilant users. The final stage is obvious, as ransomware’s trait is to threaten the user into paying the attacker’s demands as well as blocking the user’s access to their important files.

At the lower level, we hypothesise potential flows either within the same high level grouping, or across different high level groups. For example, in the stealth high level group, the process is expected to flow as follows: \(H_{FP} \Rightarrow H_{PC} \Rightarrow H_{CM}\). In other words, the typical start to end process from fingerprinting to mapping will go through propagation and communication stages in between. However, we may consider P and C as optional, which means that it is possible to have \(H_{FM}\) or \(H_{FC} \Rightarrow H_{CM}\) or \(H_{FP} \Rightarrow H_{PM}\) without going through P and/or C. In the transition between suspicious to obvious groups, the process would typically flow from \(H_{EL} \Rightarrow H_{LD} \Rightarrow H_{DT}\), as ransomware would start encrypting files in the background. When finished, the ransomware would lock the user out, and then delete traces of the original files and any processes, before finally delivering the threatening message. Nevertheless, it is possible that some ransomware variants may start showing the threatening message before encryption takes place (e.g. Donald Trump and Jigsaw ransomware), or while carrying out the encryption process at the same time (e.g. Cerber and Satana).

Preventative action hypothesis

Usually the threatening message indicates that it is obligatory to refrain from shutting down the computer, and proceed with the demands, otherwise the decryption key, user files or decryption mechanism will be lost, or payment will go up. Alternatively, ransomware that corrupts the Master Boot Record and encrypts the MFT, such as Petya instigates a reboot into the ransom note, blocking access to the operating system. Damage to the user’s environment occurs after the stealth group of stages have been deployed. We assume that all crypto-ransomware maps their target to find the files that need encryption, or to read files as part and parcel to the encrypt stage. Hence, preventative action may be more effective if it took place during the map stage.

Stopping ransomware in its tracks is fairly simple if you consider every unauthorised read or write operation on your files. However, this would entail a heavy bias toward false-positive detections of applications such as archiving tools, and hence decrease user experience and performance. There needs to be a good balance, preferably with a lower false acceptance rate for computer users. Since allowing the sample to continue past the map stage would lead to potential damage, it would be unreasonable to take action on the end-point machine.

Mapping ransomware variants to the Randep model

The Randep classifier produces graphs of timestamps of Windows API function calls per sample, as well as graphs that have been classified according to the Randep model. We analysed 18 different ransomware families, three of them (TeslaCrypt, Cerber and WannaCry) were analysed in depth, due to their high infection rate and date of discovery being around a year apart from 2015 to 2017.

Fig. 4
figure 4

TeslaCrypt propagation of states start and end times, showing a original and b the revised version


Three variants of TeslaCrypt were analysed. The key identifiers include deploying techniques to evade analysis environment, fingerprinting, communicating to known malicious IP addresses and domain names, connecting to a hidden service through TOR, injecting binaries, adding itself to the list of start-up programs, modifying the desktop wallpaper, dropping known ransom notes, replacing over 500 files, and deleting the shadow copy of user files.

Key identifiers of TeslaCrypt The Randep classifier processed the reports generated from Cuckoo Sandbox and gathered 28 signatures, which mainly involved fingerprinting, file handling, and network activity. The malware reportedly encrypted 2290 files, which was indicated through a successful call to MoveFileWithProgressW, which took place in folders including the user’s root, Desktop, Documents, Downloads, Pictures, Public, Videos, Recycle Bin, AppData, MSOCache, Program Files, and Python27. All encrypted files kept the filenames and extensions, but the .ecc extension was appended to them.

TeslaCrypt attempts to fingerprint and evade detection through various strategies including scanning registry keys and executables for the presence of anti-virus vendors and sandbox analysis systems including Cuckoo Sandbox, as well as other standard fingerprint techniques. The samples delayed the analysis for at least 4 mins 20 s, through the use of a call to NtDelayExecution, which issues a sleep command on one or more of its processes or threads.

Suspicious network activity was detected as the samples attempted to connect through a TOR gateway service at, a tor2web domain name. A tor2web URL enables users to connect to a TOR service, however; without the use of an active TOR router or browser it does not anonymise the session.

Control flow of TeslaCrypt As shown in Fig. 4a, within 1 s, TeslaCrypt deploys fingerprinting, communicating, and mapping states. This enables the initial setup of the malware to determine whether it is in a suitable environment, to establish a channel with the C&C and start the preliminary stages of the attack. Following is the locking state, in which after further inspection we notice that the malware has called NtTerminateProcess. However, it is clear this is not restricting the use of the desktop, and has been removed from the flow control graph. At 41.89 s the encrypting state follows locking, however; looking at the function calls we see an early call to GetFileInformationByHandleEx, while the rest of the functions in that state start after 428 s. Since GetFileInformationByHandleEx is a borderline function call and could also be classed in the mapping state, we have removed it from TeslaCrypt’s flow model, which amends the start of encrypting to 428.48 s. Another adjustment is to the threatening state, which started writing to the console with SendNotifyMessageW at 42.21 s, but did not draw the graphical user interface (GUI) with the ransom note until 470 s. The revised state flow model is shown in Fig. 4b with a flow in the order as follows: fingerprinting, communicating, mapping, deleting, encrypting, propagating and threatening.

Fig. 5
figure 5

Start and end times of Windows API function calls in the mapping state of the Randep model for TeslaCrypt

The flow model of TeslaCrypt has a long deployment time from mapping the user environment to the start of any suspicious or obvious class activity. Looking at the function call flow, as shown in Fig. 5, the state starts with a call to GetFileType, but most of the functions in that state are called from 41 s to 45 s. One significant function that carries out mapping is NtReadFile, which reads data from a file into a buffer, and is called 2333 times; just 43 times more than the number of files encrypted. The NtResumeThread function, which resumes a previously delayed thread, is called for the first time at 472.43 s. Shortly after, a call to DeleteFileW starts the deleting state, followed by states of encrypting and propagating. At 429.28 s, TeslaCrypt deletes the shadow copy of Window’s backups through a silent execution of the CreateProcessInternalW function with the following command line: “\({{\texttt C:\backslash Windows\backslash System32\backslash vssadmin.exe}}{\text{''}}{{\texttt delete shadows/all/Quiet}}\). The encrypting state shows the malware’s call to CryptAcquireContextW to get the handle to the cryptographic key shortly followed by MoveFileWithProgressW, which signifies the replacement of original files with ones that are encrypted. The replacement of 2290 files takes 41.27 s, i.e. approximately 55 files/s.


Key indicators of Cerber’s maliciousness include fingerprinting, self-decryption, mapping the user environment, creating files, attempting to access network shares, injecting itself into other processes, and attaching to a modified DLL. The sandbox detected a network trojan going from the sandbox to and on port 6893. The malware attempted to connect to a server with an IP range– Files were deleted, the background was changed showing the ransom note, and a notepad showed the threatening message as well as instructions how to pay and release the documents.

Key identifiers of cerber The parser gathered 22 signatures from the analysis, which mainly involved evasion, fingerprinting, networking and file handling functionality. Cerber tries to detect an analysis system through checks for the presence of Cuckoo Sandbox’s Python scripts and, whether there is any human activity, as well as the name, disk size, memory size, and other qualifying attributes of the machine. The file handling functionality involved Cerber modifying 87 files located in directories including root, AppData, Desktop, Documents and custom ones spanning from root. The modified files involved the use of function calls to MoveFileWithProgressW, where the names are scrambled and the extensions are changed to .85f0.

Fig. 6
figure 6

Cerber Ransomware start and end times of states of Randep model showing a the full-view and b the start, zoomed-in

Control flow of cerber Looking at Fig. 6a, b, we see the flow of Cerber between states that start in order of fingerprinting, mapping, propagating, communicating, encrypting, threatening, locking, and deleting. The first six states occur over 310 s sooner than locking and deleting. Figure 6b shows a zoomed-in section of the start of the process, and clearly shows the ordering of the first six states.

This sequence of events contradicts the hypothesis of the Randep model, shown in "Randep model case distinction" section. Despite encryption activating after mapping, it appears significantly close to the other states in the stealth class of the Randep model. Threatening state also appears unusually close to the stealth class, and out-of-order by coming before locking, which is in the suspicious class of the model. Further analysis of the function calls related to encryption and threatening should reveal this discrepancy with the hypothesis of the Randep model, and Cerber’s expected behaviour.

The encryption of files begins with CryptEncrypt and CryptAcquireContextW at 329 s and ends with a call to MoveFileWithProgressW, which is called from 343 s to 427 s. This means the encryption of 87 files took around 98 s, or 0.88 files/s.

The function calls of the threatening state are spread out from just after the start and almost at the end of the sample’s behaviour analysis. Most of the function calls start within 40 s after the activation of the binary, where the ones closest include LoadStringW, DrawTextExW and SendNotifyMessageW. Cerber uses LoadStringW to read parts of the accompanying JSON file that stores the configuration settings of the attack vectors. It also uses the function to feed strings into message windows, such as for social engineering a response from the victim, one example includes the following:

“No action needed. Windows found issues requiring your attention. Windows is actively checking your system for maintenance problems”.

Cerber then sends the message to the user via SendNotifyMessageW as a pop-up notification.

Fig. 7
figure 7

Cerber Ransomware start and end times of Windows API function calls within the encryption state of the Randep model

The DrawTextExW is called 53 times, 10 times at under 17 s and 43 times at 471 s, being only 3 s before the end of the sample’s activity. For the initial 10 calls, Cerber gets the date and time information and writes it to a report for communicating with the C&C. The final 43 calls are used to write the file names of the dropped ransom notes, including “R_E_A_D___T_H_I_S___6MZZ6GL_-Notepad”. Some function calls exhibited behaviour that might not fit well with the Randep model’s prediction, including CreateDirectoryW, LoadStringW and SendNotifyMessageW, and some earlier calls to DrawTextExW.

As shown in Fig. 7, the majority of the function calls for encryption are clustered from 329 s to 430 s, with the exception of CreateDirectoryW, which is not shown and is active from 1.6 s to 340.5 s. The function typically creates directories in the Windows user environment, and is not solely tied to the encryption process. Omission of this function from the Randep model would put the threatening state before encryption.

This analysis has discovered that Cerber uses function calls of LoadStringW and SendNotifyMessageW to trigger a response from the user to activate a process, which explains their early activation at 2 s and 29 s, respectively. Despite generating a warning to the user, and being obvious, they are not part of the ransom note. These two could have been placed in a new state called social engineering.

The DrawTextExW function is part of the threatening class and generates the ransom note, but also wrote to Cerber’s JSON log. This happened in two stages; feeding the log at 16 s and writing the ransom notes from 415 to 471 s.


Two samples of WannaCry were analysed. The main signatures to identify the malware’s maliciousness include its ability to unpack itself, anti-sandbox strategies, fingerprinting, manipulation of files and folders, and setup of the TOR router. Over 500 files were encrypted, the desktop background was changed to the message of the ransom, and a graphical user interface popped-up in the foreground of the user’s screen.

Another variant of WannaCry, called mssecsvc.exe was also analysed. It carries out checks on the kill-switch domain name, and scans for open RDP connections. The sandbox was setup without modifying the hosts file to make the HTTP GET request to the kill-switch timeout, and without any open RDP connections. The sample scored 3.6 out of 10, and carried out four DNS lookups on: which is the domain name used for the kill-switch. Since the address is still registered, the sample died.

The process mssecsvc.exe sends datagrams over UDP to the subnet mask of its IP block on ports 137 and 138. These ports are some of the default ones for NetBIOS, where 137 is used for the name resolution services and 138 for the datagram services. For Windows operating systems on Windows 2000 or later those ports act as a backup for the SMB service and should be blocked. Nevertheless, the malware attempts to establish a connection with another computer using NetBIOS, which is known for file and printer service sharing over an Internet connection.

Key identifiers of WannaCry WannaCry has similar attributes to most ransomware, with the exception of its propagation ability across local networks and the Internet. The report parser gathered 23 signatures, most of which are similar to those found with Cerber, with the addition of an anti-sandbox sleep mechanism, getting the network adapter’s name, installing TOR, and binding the machine’s localhost network address to listen and accept connections. The malware enforced a sleep of an average 18 min 47 s, which delayed the analysis until that time had lapsed. Afterwards, WannaCry encrypted the user’s files by mapping generic user account folders, the recycle bin, AppData and the root folder. It used RSA-AES encryption on 3129 files, appending a .WNCRY to every locked file, where the function used to replace the encrypted with originals was MoveFileWithProgressW. The malware also used WMIC.exe to get and delete the shadow copy of the user’s files.

Control flow of WannaCry Due to the modular approach of WannaCry’s implementation, and the use of threads to carry out processes, we see all states apart from deleting starting before a second has passed. Looking at the flow of states, mapping and threatening are the first to start; both begin at 32 ms, shortly followed by encryption at 94 ms. Thereafter it follows: communicating, fingerprinting, propagating, and locking, finishing with deleting at 2.84 s.

Fingerprinting starts much later than predicted by the hypothesis, which said it would start first. The initial part of fingerprinting would be the check to the kill-switch domain, however; the function calls involved with that process are considered communication states. Accordingly, communication passes the domain name as a parameter and calls InternetOpenA and WSAStartup as the first function call in the mssecsvc.exe’s analysis; see the graph in Fig. 8c. Prior to starting encryption, WannaCry fingerprints the system information with calls to GetNativeSystemInfo, it also gets the system time, and memory status. The memory check could be a requirements check for starting the encryption process, or just to detect the presence of a sandboxed environment.

The communication state creates a server and binds it to after 87 s, which WannaCry uses to send and receive packets over the TOR network. The malware uses TOR in an attempt to anonymize its network data, and to avoid detection. At 106.59 s, the malware makes a call to LookupPrivilegeValueW, which gets the privilege value and name of the logged-on user’s locally unique identifier (LUID). In the propagation state we see the use of OpenSCManager after 107 s, which opens a connection and the service control manager database on a given computer. Then after 17 s the local server is shutdown.

Fig. 8
figure 8

Randep states of WannaCry ransomware, showing a full-view, b zoomed-in, and c WannaCry’s mssecsvc.exe process analysis showing communicating functions

WannaCry starts encryption early with a call to SetFileTime, it then sets up a new handle for the Cryptographic API functions, and decrypts a 16-byte string. The encryption of files begins at 2.84 s with a call to CryptGenKey, CryptExportKey and CryptEncrypt (see Fig. 9). CryptEncrypt carries out the encryption of the files from 2.84 to 60.83 s. The encrypted contents are temporarily stored in the system’s default temporary folder, and the encrypted files replace the originals with a call to MoveFileWithProgressW at 3.68 s. The encryption ends when the original file has been replaced, which is noted by the end of MoveFileWithProgressW at 143.88 s. Hence the 3129 files encrypted took around 141 s, i.e. 22 files/s.

The malware spawns a cmd.exe process without showing the window to quietly delete the shadow copy of the file system, as follows:

figure a

The command is executed at 104.69 s, but the process is created later at 116.55 s.

The first time that the user becomes aware of the threat is when the malware makes a call to DrawTextExW 86.87 s, with a buffer containing Wana Decryptor 2.0, which is the window title of the GUI shown to the victim. Later calls show that the left hand side of the GUI is populated first with two countdown timers and call to actions including “Time Left” and “Payment will be raised on”. This technique attempts to create a sense of urgency in the victim meeting the attacker’s demands.

Fig. 9
figure 9

Encryption states of WannaCry Ransomware

Comparing the three ransomware samples in the Randep model

To compare the behaviour of these three ransomware strains (TeslaCrypt, Cerber and WannaCry), we produce a graph mapping a sequence of events (from 0 to 7) for these strains according to the Randep model. Figure 10 shows that out of the eight states, none of the three ransomware strains match completely, six have pairings, and two have no matches across the board, which backs up the Case Distinction discussed in "Randep model case distinction" section. TeslaCrypt and Cerber both put fingerprinting at stage 0 and encrypting at stage 4, which fits with the null hypothesis. All three put communicating and mapping between stage 0 and 3, which fits with the hypothesis of the higher level of the Randep model. All that showed signs of locking put it between stage 6 and 7, fitting in the obvious class of the Randep model. Additionally, all carried out mapping prior to encryption. Therefore, early warning signs of crypto-ransomware is through the use of mapping API functions.

Fig. 10
figure 10

Graph showing the stages of deployment for TeslaCrypt, Cerber and WannaCry according to the states of the Randep model

Table 1 Number of infected devices

Results and analysis from the user study

Out of 1090 potential respondents contacted, 147 acknowledged our request, 72 agreed to participate, although only 46 gave a response in the questionnaire in the end. Out of these 46 respondents, 28 said that they had experienced at least one ransomware attack.

From the respondents, eight volunteered to participate in an interview; four universities, three SME companies and one student. In the following sub-sections, the results from the questionnaire are presented in the form of graphs, and the highlights from the interviews are summarised.

Analysis of the data from the user study

The first questions in the questionnaire were to do with the approximate date of the attack, the operating system of the infected device and the way ransomware was suspected to have entered the network. In 27 out of 48 cases, a device with Windows 7 operating system was involved (Fig. 11). Some responses included more than one operating system, hence the number of answers in this graph exceed the number of total responses (those attacked) for the questionnaire.

Fig. 11
figure 11

Breakdown of operating systems affected by ransomware

The ransomware entry method was enquired only in the questionnaires for universities and companies. A total of 28 responses were received for this question (compulsory question), of which 6 chose unknown. As Fig. 12 presents, the majority (64.3%) stated that the ransomware entered from a malicious email message; malicious attachment (35.7%) being more common than a malicious link (28.6%).

Fig. 12
figure 12

Ransomware entry method

In 63% of the cases reported in our study, the ransomware did not propagate; infection was limited to only one device within the organisations (Table 1). Nearly 77% of respondents could access their files after the attack. In 69.7% of the cases, the means to recover files was from backup, only one respondent having paid the ransom (Fig. 13).

Fig. 13
figure 13

Recovery after ransomware incident

The most common first signs of infection reported were the desktop being locked, files going missing and Microsoft Office software crashing or failing to open files (see Table 2 for the full list of infection signs).

Students were asked an additional question on whether the term “ransomware” was familiar to them. Out of 50 respondents, 28 (56%) answered “no”.


We had the chance to interview four security experts from universities and three from SMEs. Also, one student agreed to give an interview. In the student interview, the questions focused on gaining a deeper understanding of how the attack occurred and what, if any, were the lessons learned. The questions for the experts were more technical (e.g. also querying the organisations’ defences against malicious attacks), given the level of experience they had.

The student’s ransomware incident was a case where the device got locked after attempting to watch videos online. The ransom message included a loud noise demanding attention, stating that device has been locked, accompanied by a phone number for technical support to unlock the device. The “technical support” posed as a Microsoft team and demanded a payment for their services. The person on the phone got remote access on the device and seemingly unlocked it. The victim felt the loud noise made the situation more threatening and caused a panic reaction making them call the number immediately. The message did not include a demand for a ransom payment, the money was only asked on the phone. At the time, the victim did not have an external backup, but as a lesson learned, they are now more aware of the importance of basic security hygiene, including having a regular external backup.

Based on the interviews, universities seem more likely to be targeted by ransomware than companies. University staff contact details, including email addresses, are commonly available online, making targeted attacks easier. An IT expert from one university stated that emails represent approximately three quarters of the attack vectors. They mentioned that some attackers even used email address spoofing in their attack.

Table 2 First signs of ransomware infection

Among the interviewed organisations, a pattern could be observed. In most cases, the organisations had had only basic defences in place prior to them being infected by ransomware. These defences include a firewall and anti-virus software. Most had implemented or were in the process of implementing more advanced systems. A new tool that was brought up in the interviews was Sophos InterceptX, including CryptoGuard capabilities. Also, in addition to systems and software, the organisations were putting emphasis on enhancing processes and user education on security issues.

In respect of technical solutions, the common opinion among experts was that endpoint security should be prioritised. Many attacks are successfully stopped at the network level. With current tools, malicious attachments are mostly captured before they reach the end user. Due to this, when it comes to phishing, attackers are focusing increasingly on email links rather than attachments. This trend also highlights the importance of user education to prevent clicking of malicious links. It was also said that global headlines on ransomware attacks have helped bring awareness and raise interest in the topic among users. The majority of the contacted organisations were planning to improve staff/student training further.

During one interview, an important viewpoint was brought to our attention regarding admin policies. Running everyday operations with admin privileges gives ransomware more capabilities to operate on the device if infected. Lower privileges can limit, if not stop, the damage a ransomware attack can cause. Many of the interviewed organisations were in the middle of restricting the policies for giving out admin policies.


In this work, we analysed 18 families of ransomware in order to come up with a model for ransomware deployment we call Randep. The model was developed from background knowledge of Windows APIs, common ransomware traits, and threat intelligence of ransomware authors’ evolving strategies. At the higher level, there are three phases in ransomware execution, starting from stealth operations, to suspicious activities, and finally obvious actions. Each of these higher level stages may be composed of several lower level stages, which are probabilistic in nature (by this we mean not all ransomware will exhibit all of them, and the sequence of actions involving these stages may differ). The stealth stage includes fingerprinting, propagating, communicating, and mapping. The suspicious stage includes encrypting and locking activities, while the obvious stage involves deleting and threatening actions.

We have identified the mapping stage as an early warning sign prior to encryption, hence for a more effective solution, we recommend to put in place countermeasures that can be activated before the mapping activities are completed. Surprisingly, most of the ransomware families exhibited some form of fingerprinting, and this could be local or remote diagnosis of the machine.

This paper also presents a user study into ransomware deployment through questionnaire and in-depth interview involving stakeholders from universities and SMEs. Ransomware developers have numerous ways to execute attacks. Based on our research, in the past few years the most common attack vector has been via email, more specifically through email attachments. However, the experts interviewed in this research suggested that attackers are moving more into using email links due to the increased use of tools filtering out suspicious attachments from emails. In the interviews, experts pointed out that user education and endpoint security are the most important focus points in fighting ransomware, due to email still being highly used in ransomware distribution. Another matter to consider in organisations is the process of handing out admin privileges.

Also worth noting is the proportionally high number of cases where the ransomware entry method was unknown to the user. This phenomenon came up in many of the interviews as well: ransomware often resembles normal user activity and does not announce itself until files have been encrypted and a ransom note is displayed. Also, some variants may sleep before activating, making the effort to trace back to the entry point challenging. One of the most common first signs of infection was that the desktop was locked. In many cases, when the first sign is observed, it is already too late. Other common signs were missing files and being unable to open files. These signs can be viewed as red flags and should lead to an immediate reaction. If noticed in time, damage may be limited.

The results validate the importance of extensive backup. Having an off-line backup in a separate location is one of the best ways to ensure the safety of data. In most cases post infection, the affected device needs to be wiped clean and rebuilt. A promising trend observed from our user study is that only in one case was the ransom demand being paid. Paying the ransom does not guarantee decryption of files and only finances criminals for further attacks.

One of the goals of conducting this research was spreading the knowledge of the threat that ransomware imposes, especially to younger people such as university students. This proved to be a sensible goal as 56% of students who took part in our study were not familiar with the term prior to the questionnaire. However, the questionnaire was delivered to the students before the WannaCry ransomware incident affecting the UK National Health Service became a headline news. Were the responses given after the attack, the results would likely have been quite different.

Threat intelligence predicts ransomware attacks will continue to rise. However, with insight and analysis into the behaviour of ransomware, we should be able to identify key areas to thwart any incoming attack. The Randep model can act as a template to illustrate the stages of deployment of ransomware, and it can be used as an agent for detecting early warning signs of variants of ransomware.

Future work

We will conduct a detailed analysis of the timing and the sequence pattern of the stages of ransomware deployment in order to come up with effective countermeasures for the characteristics exhibited.

The Randep model could be further validated with more ransomware samples, as well as testing the detection of early warning signs when submitting benign programs that carry out encryption, such as WinZip.

Furthermore, other threat intelligence modelling such as Cyber Kill Chain [which has been shown by Kiwia et al. (2017) to be useful for creating a taxonomy that can be used for detecting and mitigating banking trojans] can be integrated into the Randep model to improve its accuracy. This will also require more ransomware samples to be collected and analysed, in order to develop a more up-to-date ransomware taxonomy.

The API scraper decreased the load for classifying APIs into stages for the Randep model, which was carried out manually, but could also be done automatically through machine learning. A text classifier could parse the description generated by the API scraper to place it into a suitable stage. This would further increase the autonomy of the system, enabling classification on the fly.






Advanced Encryption Standard


Application Programming Interface


Command and Control


Dynamic Linked Library


Graphical User Interface




Locally Unique Identifier


Master File Table


Operating System




Ransomware Deployment


Small and Medium-sized Enterprise


Virtual Machine


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Authors' contributions

All of the work presented in this paper is part of the MSc project at the School of Computing, University of Kent by Gavin Hull and Henna John, both were supervised by Budi Arief. All authors read and approved the final manuscript.


Part of the work presented in this paper has been funded by the UK Engineering and Physical Sciences Research Council (EPSRC) Project EP/P011772/1 on the EconoMical, PsycHologicAl and Societal Impact of RanSomware (EMPHASIS).

Competing interests

The authors declare that they have no competing interests. The views and opinions expressed are of those of the authors and do not necessarily reflect the views and opinions of Deloitte LLP, Accenture, or the University of Kent.

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The data, tables and figures presented in the paper are embedded directly into the pdf file of the submission. These are available to be supplied separately if needed.


Budi Arief receives funding from the UK EPSRC project EP/P011772/1 on the EconoMical, PsycHologicAl and Societal Impact of RanSomware (EMPHASIS).

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Hull, G., John, H. & Arief, B. Ransomware deployment methods and analysis: views from a predictive model and human responses. Crime Sci 8, 2 (2019).

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