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Poudyal, Subash, Dasgupta, Dipankar.  2020.  AI-Powered Ransomware Detection Framework. 2020 IEEE Symposium Series on Computational Intelligence (SSCI). :1154—1161.

Ransomware attacks are taking advantage of the ongoing pandemics and attacking the vulnerable systems in business, health sector, education, insurance, bank, and government sectors. Various approaches have been proposed to combat ransomware, but the dynamic nature of malware writers often bypasses the security checkpoints. There are commercial tools available in the market for ransomware analysis and detection, but their performance is questionable. This paper aims at proposing an AI-based ransomware detection framework and designing a detection tool (AIRaD) using a combination of both static and dynamic malware analysis techniques. Dynamic binary instrumentation is done using PIN tool, function call trace is analyzed leveraging Cuckoo sandbox and Ghidra. Features extracted at DLL, function call, and assembly level are processed with NLP, association rule mining techniques and fed to different machine learning classifiers. Support vector machine and Adaboost with J48 algorithms achieved the highest accuracy of 99.54% with 0.005 false-positive rates for a multi-level combined term frequency approach.

Ayub, M. A., Continella, A., Siraj, A..  2020.  An I/O Request Packet (IRP) Driven Effective Ransomware Detection Scheme using Artificial Neural Network. 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI). :319–324.
In recent times, there has been a global surge of ransomware attacks targeted at industries of various types and sizes from retail to critical infrastructure. Ransomware researchers are constantly coming across new kinds of ransomware samples every day and discovering novel ransomware families out in the wild. To mitigate this ever-growing menace, academia and industry-based security researchers have been utilizing unique ways to defend against this type of cyber-attacks. I/O Request Packet (IRP), a low-level file system I/O log, is a newly found research paradigm for defense against ransomware that is being explored frequently. As such in this study, to learn granular level, actionable insights of ransomware behavior, we analyze the IRP logs of 272 ransomware samples belonging to 18 different ransomware families captured during individual execution. We further our analysis by building an effective Artificial Neural Network (ANN) structure for successful ransomware detection by learning the underlying patterns of the IRP logs. We evaluate the ANN model with three different experimental settings to prove the effectiveness of our approach. The model demonstrates outstanding performance in terms of accuracy, precision score, recall score, and F1 score, i.e., in the range of 99.7%±0.2%.
Ganfure, G. O., Wu, C.-F., Chang, Y.-H., Shih, W.-K..  2020.  DeepGuard: Deep Generative User-behavior Analytics for Ransomware Detection. 2020 IEEE International Conference on Intelligence and Security Informatics (ISI). :1—6.

In the last couple of years, the move to cyberspace provides a fertile environment for ransomware criminals like ever before. Notably, since the introduction of WannaCry, numerous ransomware detection solution has been proposed. However, the ransomware incidence report shows that most organizations impacted by ransomware are running state of the art ransomware detection tools. Hence, an alternative solution is an urgent requirement as the existing detection models are not sufficient to spot emerging ransomware treat. With this motivation, our work proposes "DeepGuard," a novel concept of modeling user behavior for ransomware detection. The main idea is to log the file-interaction pattern of typical user activity and pass it through deep generative autoencoder architecture to recreate the input. With sufficient training data, the model can learn how to reconstruct typical user activity (or input) with minimal reconstruction error. Hence, by applying the three-sigma limit rule on the model's output, DeepGuard can distinguish the ransomware activity from the user activity. The experiment result shows that DeepGuard effectively detects a variant class of ransomware with minimal false-positive rates. Overall, modeling the attack detection with user-behavior permits the proposed strategy to have deep visibility of various ransomware families.

Bajpai, P., Enbody, R..  2020.  Attacking Key Management in Ransomware. IT Professional. 22:21—27.

Ransomware have observed a steady growth over the years with several concerning trends that indicate efficient, targeted attacks against organizations and individuals alike. These opportunistic attackers indiscriminately target both public and private sector entities to maximize gain. In this article, we highlight the criticality of key management in ransomware's cryptosystem in order to facilitate building effective solutions against this threat. We introduce the ransomware kill chain to elucidate the path our adversaries must take to attain their malicious objective. We examine current solutions presented against ransomware in light of this kill chain and specify which constraints on ransomware are being violated by the existing solutions. Finally, we present the notion of memory attacks against ransomware's key management and present our initial experiments with dynamically extracting decryption keys from real-world ransomware. Results of our preliminary research are promising and the extracted keys were successfully deployed in subsequent data decryption.

Lee, Y., Woo, S., Song, Y., Lee, J., Lee, D. H..  2020.  Practical Vulnerability-Information-Sharing Architecture for Automotive Security-Risk Analysis. IEEE Access. 8:120009—120018.
Emerging trends that are shaping the future of the automotive industry include electrification, autonomous driving, sharing, and connectivity, and these trends keep changing annually. Thus, the automotive industry is shifting from mechanical devices to electronic control devices, and is not moving to Internet of Things devices connected to 5G networks. Owing to the convergence of automobile-information and communication technology (ICT), the safety and convenience features of automobiles have improved significantly. However, cyberattacks that occur in the existing ICT environment and can occur in the upcoming 5G network are being replicated in the automobile environment. In a hyper-connected society where 5G networks are commercially available, automotive security is extremely important, as vehicles become the center of vehicle to everything (V2X) communication connected to everything around them. Designing, developing, and deploying information security techniques for vehicles require a systematic security-risk-assessment and management process throughout the vehicle's lifecycle. To do this, a security risk analysis (SRA) must be performed, which requires an analysis of cyber threats on automotive vehicles. In this study, we introduce a cyber kill chain-based cyberattack analysis method to create a formal vulnerability-analysis system. We can also analyze car-hacking studies that were conducted on real cars to identify the characteristics of the attack stages of existing car-hacking techniques and propose the minimum but essential measures for defense. Finally, we propose an automotive common-vulnerabilities-and-exposure system to manage and share evolving vehicle-related cyberattacks, threats, and vulnerabilities.
Tanana, D., Tanana, G..  2020.  Advanced Behavior-Based Technique for Cryptojacking Malware Detection. 2020 14th International Conference on Signal Processing and Communication Systems (ICSPCS). :1—4.
With rising value and popularity of cryptocurrencies, they inevitably attract cybercriminals seeking illicit profits within blockchain ecosystem. Two of the most popular methods are ransomware and cryptojacking. Ransomware, being the first and more obvious threat has been extensively studied in the past. Unlike that, scientists have often neglected cryptojacking, because it’s less obvious and less harmful than ransomware. In this paper, we’d like to propose enhanced detection program to combat cryptojacking, additionally briefly touching history of cryptojacking, also known as malicious mining and reviewing most notable previous attempts to detect and combat cryptojacking. The review would include out previous work on malicious mining detection and our current detection program is based on its previous iteration, which mostly used CPU usage heuristics to detect cryptojacking. However, we will include additional metrics for malicious mining detection, such as network usage and calls to cryptographic libraries, which result in a 93% detection rate against the selected number of cryptojacking samples, compared to 81% rate achieved in previous work. Finally, we’ll discuss generalization of proposed detection technique to include GPU cryptojackers.
Tanana, D..  2020.  Behavior-Based Detection of Cryptojacking Malware. 2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT). :0543—0545.
With rise of cryptocurrency popularity and value, more and more cybercriminals seek to profit using that new technology. Most common ways to obtain illegitimate profit using cryptocurrencies are ransomware and cryptojacking also known as malicious mining. And while ransomware is well-known and well-studied threat which is obvious by design, cryptojacking is often neglected because it's less harmful and much harder to detect. This article considers question of cryptojacking detection. Brief history and definition of cryptojacking are described as well as reasons for designing custom detection technique. We also propose complex detection technique based on CPU load by an application, which can be applied to both browser-based and executable-type cryptojacking samples. Prototype detection program based on our technique was designed using decision tree algorithm. The program was tested in a controlled virtual machine environment and achieved 82% success rate against selected number of cryptojacking samples. Finally, we'll discuss generalization of proposed technique for future work.
Mani, G., Pasumarti, V., Bhargava, B., Vora, F. T., MacDonald, J., King, J., Kobes, J..  2020.  DeCrypto Pro: Deep Learning Based Cryptomining Malware Detection Using Performance Counters. 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS). :109—118.
Autonomy in cybersystems depends on their ability to be self-aware by understanding the intent of services and applications that are running on those systems. In case of mission-critical cybersystems that are deployed in dynamic and unpredictable environments, the newly integrated unknown applications or services can either be benign and essential for the mission or they can be cyberattacks. In some cases, these cyberattacks are evasive Advanced Persistent Threats (APTs) where the attackers remain undetected for reconnaissance in order to ascertain system features for an attack e.g. Trojan Laziok. In other cases, the attackers can use the system only for computing e.g. cryptomining malware. APTs such as cryptomining malware neither disrupt normal system functionalities nor trigger any warning signs because they simply perform bitwise and cryptographic operations as any other benign compression or encoding application. Thus, it is difficult for defense mechanisms such as antivirus applications to detect these attacks. In this paper, we propose an Operating Context profiling system based on deep neural networks-Long Short-Term Memory (LSTM) networks-using Windows Performance Counters data for detecting these evasive cryptomining applications. In addition, we propose Deep Cryptomining Profiler (DeCrypto Pro), a detection system with a novel model selection framework containing a utility function that can select a classification model for behavior profiling from both the light-weight machine learning models (Random Forest and k-Nearest Neighbors) and a deep learning model (LSTM), depending on available computing resources. Given data from performance counters, we show that individual models perform with high accuracy and can be trained with limited training data. We also show that the DeCrypto Profiler framework reduces the use of computational resources and accurately detects cryptomining applications by selecting an appropriate model, given the constraints such as data sample size and system configuration.
Naik, N., Jenkins, P., Savage, N., Yang, L., Naik, K., Song, J..  2020.  Embedding Fuzzy Rules with YARA Rules for Performance Optimisation of Malware Analysis. 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–7.
YARA rules utilises string or pattern matching to perform malware analysis and is one of the most effective methods in use today. However, its effectiveness is dependent on the quality and quantity of YARA rules employed in the analysis. This can be managed through the rule optimisation process, although, this may not necessarily guarantee effective utilisation of YARA rules and its generated findings during its execution phase, as the main focus of YARA rules is in determining whether to trigger a rule or not, for a suspect sample after examining its rule condition. YARA rule conditions are Boolean expressions, mostly focused on the binary outcome of the malware analysis, which may limit the optimised use of YARA rules and its findings despite generating significant information during the execution phase. Therefore, this paper proposes embedding fuzzy rules with YARA rules to optimise its performance during the execution phase. Fuzzy rules can manage imprecise and incomplete data and encompass a broad range of conditions, which may not be possible in Boolean logic. This embedding may be more advantageous when the YARA rules become more complex, resulting in multiple complex conditions, which may not be processed efficiently utilising Boolean expressions alone, thus compromising effective decision-making. This proposed embedded approach is applied on a collected malware corpus and is tested against the standard and enhanced YARA rules to demonstrate its success.
Naik, N., Jenkins, P., Savage, N., Yang, L., Boongoen, T., Iam-On, N..  2020.  Fuzzy-Import Hashing: A Malware Analysis Approach. 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–8.
Malware has remained a consistent threat since its emergence, growing into a plethora of types and in large numbers. In recent years, numerous new malware variants have enabled the identification of new attack surfaces and vectors, and have become a major challenge to security experts, driving the enhancement and development of new malware analysis techniques to contain the contagion. One of the preliminary steps of malware analysis is to remove the abundance of counterfeit malware samples from the large collection of suspicious samples. This process assists in the management of man and machine resources effectively in the analysis of both unknown and likely malware samples. Hashing techniques are one of the fastest and efficient techniques for performing this preliminary analysis such as fuzzy hashing and import hashing. However, both hashing methods have their limitations and they may not be effective on their own, instead the combination of two distinctive methods may assist in improving the detection accuracy and overall performance of the analysis. This paper proposes a Fuzzy-Import hashing technique which is the combination of fuzzy hashing and import hashing to improve the detection accuracy and overall performance of malware analysis. This proposed Fuzzy-Import hashing offers several benefits which are demonstrated through the experimentation performed on the collected malware samples and compared against stand-alone techniques of fuzzy hashing and import hashing.
Ko, Ju-Seong, Jo, Jeong-Seok, Kim, Deuk-Hun, Choi, Seul-Ki, Kwak, Jin.  2019.  Real Time Android Ransomware Detection by Analyzed Android Applications. 2019 International Conference on Electronics, Information, and Communication (ICEIC). :1–5.
Recently, damage caused by ransomware has been increasing in PC and Android environments. There are many studies into real-time ransomware detection because the most important time to prevent encryption is before ransomware is able to execute its malicious process. Traditional analyses determine an application is ransomware or not by static/dynamic methods. Those analyses can serve as components of a method to detect ransomware in real time. However, problems can occur such as the inability to detect new/variant/unknown ransomware. These types require signed patches from a trusted party that can only be created after attacks occur. In a previous study into realtime new/variant/unknown ransomware detection in a PC environment, important files are monitored and only programs that have been previously analyzed and evaluated as nonmalicious are allowed. As such, programs that have not been analyzed are restricted from accessing important files. In an Android environment, this method can be applied using Android applications to prevent emerging threats and verify consistency with user intent. Thus, this paper proposes a method of detecting new/variant/unknown ransomware in real time in an Android environment.
Javed Butt, Usman, Abbod, Maysam, Lors, Anzor, Jahankhani, Hamid, Jamal, Arshad, Kumar, Arvind.  2019.  Ransomware Threat and its Impact on SCADA. 2019 IEEE 12th International Conference on Global Security, Safety and Sustainability (ICGS3). :205—212.
Modern cybercrimes have exponentially grown over the last one decade. Ransomware is one of the types of malware which is the result of sophisticated attempt to compromise the modern computer systems. The governments and large corporations are investing heavily to combat this cyber threat against their critical infrastructure. It has been observed that over the last few years that Industrial Control Systems (ICS) have become the main target of Ransomware due to the sensitive operations involved in the day to day processes of these industries. As the technology is evolving, more and more traditional industrial systems are replaced with advanced industry methods involving advanced technologies such as Internet of Things (IoT). These technology shift help improve business productivity and keep the company's global competitive in an overflowing competitive market. However, the systems involved need secure measures to protect integrity and availability which will help avoid any malfunctioning to their operations due to the cyber-attacks. There have been several cyber-attack incidents on healthcare, pharmaceutical, water cleaning and energy sector. These ICS' s are operated by remote control facilities and variety of other devices such as programmable logic controllers (PLC) and sensors to make a network. Cyber criminals are exploring vulnerabilities in the design of these ICS's to take the command and control of these systems and disrupt daily operations until ransomware is paid. This paper will provide critical analysis of the impact of Ransomware threat on SCADA systems.
Jin, Yong, Tomoishi, Masahiko.  2019.  Encrypted QR Code Based Optical Challenge-Response Authentication by Mobile Devices for Mounting Concealed File System. 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). 2:676–681.
Nowadays mobile devices have become the majority terminals used by people for social activities so that carrying business data and private information in them have become normal. Accordingly, the risk of data related cyber attacks has become one of the most critical security concerns. The main purpose of this work is to mitigate the risk of data breaches and damages caused by malware and the lost of mobile devices. In this paper, we propose an encrypted QR code based optical challenge-response authentication by mobile devices for mounting concealed file systems. The concealed file system is basically invisible to the users unless being successfully mounted. The proposed authentication scheme practically applies cryptography and QR code technologies to challenge-response scheme in order to secure the concealed file system. The key contribution of this work is to clarify a possibility of a mounting authentication scheme involving two mobile devices using a special optical communication way (QR code exchanges) which can be realizable without involving any network accesses. We implemented a prototype system and based on the preliminary feature evaluations results we confirmed that encrypted QR code based optical challenge-response is possible between a laptop and a smart phone and it can be applied to authentication for mounting concealed file systems.
Naik, Nitin, Jenkins, Paul, Savage, Nick.  2019.  A Ransomware Detection Method Using Fuzzy Hashing for Mitigating the Risk of Occlusion of Information Systems. 2019 International Symposium on Systems Engineering (ISSE). :1–6.
Today, a significant threat to organisational information systems is ransomware that can completely occlude the information system by denying access to its data. To reduce this exposure and damage from ransomware attacks, organisations are obliged to concentrate explicitly on the threat of ransomware, alongside their malware prevention strategy. In attempting to prevent the escalation of ransomware attacks, it is important to account for their polymorphic behaviour and dispersion of inexhaustible versions. However, a number of ransomware samples possess similarity as they are created by similar groups of threat actors. A particular threat actor or group often adopts similar practices or codebase to create unlimited versions of their ransomware. As a result of these common traits and codebase, it is probable that new or unknown ransomware variants can be detected based on a comparison with their originating or existing samples. Therefore, this paper presents a detection method for ransomware by employing a similarity preserving hashing method called fuzzy hashing. This detection method is applied on the collected WannaCry or WannaCryptor ransomware corpus utilising three fuzzy hashing methods SSDEEP, SDHASH and mvHASH-B to evaluate the similarity detection success rate by each method. Moreover, their fuzzy similarity scores are utilised to cluster the collected ransomware corpus and its results are compared to determine the relative accuracy of the selected fuzzy hashing methods.
Alzahrani, Abdulrahman, Alshahrani, Hani, Alshehri, Ali, Fu, Huirong.  2019.  An Intelligent Behavior-Based Ransomware Detection System For Android Platform. 2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA). :28–35.

Malware variants exhibit polymorphic attacks due to the tremendous growth of the present technologies. For instance, ransomware, an astonishingly growing set of monetary-gain threats in the recent years, is peculiarized as one of the most treacherous cyberthreats against innocent individuals and businesses by locking their devices and/or encrypting their files. Many proposed attempts have been introduced by cybersecurity researchers aiming at mitigating the epidemic of the ransomware attacks. However, this type of malware is kept refined by utilizing new evasion techniques, such as sophisticated codes, dynamic payloads, and anti-emulation techniques, in order to survive against detection systems. This paper introduces RanDetector, a new automated and lightweight system for detecting ransomware applications in Android platform based on their behavior. In particular, this detection system investigates the appearance of some information that is related to ransomware operations in an inspected application before integrating some supervised machine learning models to classify the application. RanDetector is evaluated and tested on a dataset of more 450 applications, including benign and ransomware. Hence, RanDetector has successfully achieved more that 97.62% detection rate with nearly zero false positive.

Hirano, Manabu, Kobayashi, Ryotaro.  2019.  Machine Learning Based Ransomware Detection Using Storage Access Patterns Obtained From Live-forensic Hypervisor. 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS). :1–6.
With the rapid increase in the number of Internet of Things (IoT) devices, mobile devices, cloud services, and cyber-physical systems, the large-scale cyber attacks on enterprises and public sectors have increased. In particular, ransomware attacks damaged UK's National Health Service and many enterprises around the world in 2017. Therefore, researchers have proposed ransomware detection and prevention systems. However, manual inspection in static and dynamic ransomware analysis is time-consuming and it cannot cope with the rapid increase in variants of ransomware family. Recently, machine learning has been used to automate ransomware analysis by creating a behavioral model of same ransomware family. To create effective behavioral models of ransomware, we first obtained storage access patterns of live ransomware samples and of a benign application by using a live-forensic hypervisor called WaybackVisor. To distinguish ransomware from a benign application that has similar behavior to ransomware, we carefully selected five dimensional features that were extracted both from actual ransomware's Input and Output (I/O) logs and from a benign program's I/O logs. We created and evaluated machine learning models by using Random Forest, Support Vector Machine, and K-Nearest Neighbors. Our experiments using the proposed five features of storage access patterns achieved F-measure rate of 98%.
Noorbehbahani, Fakhroddin, Rasouli, Farzaneh, Saberi, Mohammad.  2019.  Analysis of Machine Learning Techniques for Ransomware Detection. 2019 16th International ISC (Iranian Society of Cryptology) Conference on Information Security and Cryptology (ISCISC). :128–133.

In parallel with the increasing growth of the Internet and computer networks, the number of malwares has been increasing every day. Today, one of the newest attacks and the biggest threats in cybersecurity is ransomware. The effectiveness of applying machine learning techniques for malware detection has been explored in much scientific research, however, there is few studies focused on machine learning-based ransomware detection. In this paper, the effectiveness of ransomware detection using machine learning methods applied to CICAndMal2017 dataset is examined in two experiments. First, the classifiers are trained on a single dataset containing different types of ransomware. Second, different classifiers are trained on datasets of 10 ransomware families distinctly. Our findings imply that in both experiments random forest outperforms other tested classifiers and the performance of the classifiers are not changed significantly when they are trained on each family distinctly. Therefore, the random forest classification method is very effective in ransomware detection.

Naik, Nitin, Jenkins, Paul, Gillett, Jonathan, Mouratidis, Haralambos, Naik, Kshirasagar, Song, Jingping.  2019.  Lockout-Tagout Ransomware: A Detection Method for Ransomware using Fuzzy Hashing and Clustering. 2019 IEEE Symposium Series on Computational Intelligence (SSCI). :641–648.

Ransomware attacks are a prevalent cybersecurity threat to every user and enterprise today. This is attributed to their polymorphic behaviour and dispersion of inexhaustible versions due to the same ransomware family or threat actor. A certain ransomware family or threat actor repeatedly utilises nearly the same style or codebase to create a vast number of ransomware versions. Therefore, it is essential for users and enterprises to keep well-informed about this threat landscape and adopt proactive prevention strategies to minimise its spread and affects. This requires a technique to detect ransomware samples to determine the similarity and link with the known ransomware family or threat actor. Therefore, this paper presents a detection method for ransomware by employing a combination of a similarity preserving hashing method called fuzzy hashing and a clustering method. This detection method is applied on the collected WannaCry/WannaCryptor ransomware samples utilising a range of fuzzy hashing and clustering methods. The clustering results of various clustering methods are evaluated through the use of the internal evaluation indexes to determine the accuracy and consistency of their clustering results, thus the effective combination of fuzzy hashing and clustering method as applied to the particular ransomware corpus. The proposed detection method is a static analysis method, which requires fewer computational overheads and performs rapid comparative analysis with respect to other static analysis methods.

Bibi, Iram, Akhunzada, Adnan, Malik, Jahanzaib, Ahmed, Ghufran, Raza, Mohsin.  2019.  An Effective Android Ransomware Detection Through Multi-Factor Feature Filtration and Recurrent Neural Network. 2019 UK/ China Emerging Technologies (UCET). :1–4.
With the increasing diversity of Android malware, the effectiveness of conventional defense mechanisms are at risk. This situation has endorsed a notable interest in the improvement of the exactitude and scalability of malware detection for smart devices. In this study, we have proposed an effective deep learning-based malware detection model for competent and improved ransomware detection in Android environment by looking at the algorithm of Long Short-Term Memory (LSTM). The feature selection has been done using 8 different feature selection algorithms. The 19 important features are selected through simple majority voting process by comparing results of all feature filtration techniques. The proposed algorithm is evaluated using android malware dataset (CI-CAndMal2017) and standard performance parameters. The proposed model outperforms with 97.08% detection accuracy. Based on outstanding performance, we endorse our proposed algorithm to be efficient in malware and forensic analysis.
Bahrani, Ala, Bidgly, Amir Jalaly.  2019.  Ransomware detection using process mining and classification algorithms. 2019 16th International ISC (Iranian Society of Cryptology) Conference on Information Security and Cryptology (ISCISC). :73–77.

The fast growing of ransomware attacks has become a serious threat for companies, governments and internet users, in recent years. The increasing of computing power, memory and etc. and the advance in cryptography has caused the complicating the ransomware attacks. Therefore, effective methods are required to deal with ransomwares. Although, there are many methods proposed for ransomware detection, but these methods are inefficient in detection ransomwares, and more researches are still required in this field. In this paper, we have proposed a novel method for identify ransomware from benign software using process mining methods. The proposed method uses process mining to discover the process model from the events logs, and then extracts features from this process model and using these features and classification algorithms to classify ransomwares. This paper shows that the use of classification algorithms along with the process mining can be suitable to identify ransomware. The accuracy and performance of our proposed method is evaluated using a study of 21 ransomware families and some benign samples. The results show j48 and random forest algorithms have the best accuracy in our method and can achieve to 95% accuracy in detecting ransomwares.

Naik, Nitin, Jenkins, Paul, Savage, Nick, Yang, Longzhi.  2019.  Cyberthreat Hunting - Part 1: Triaging Ransomware using Fuzzy Hashing, Import Hashing and YARA Rules. 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–6.

Ransomware is currently one of the most significant cyberthreats to both national infrastructure and the individual, often requiring severe treatment as an antidote. Triaging ran-somware based on its similarity with well-known ransomware samples is an imperative preliminary step in preventing a ransomware pandemic. Selecting the most appropriate triaging method can improve the precision of further static and dynamic analysis in addition to saving significant t ime a nd e ffort. Currently, the most popular and proven triaging methods are fuzzy hashing, import hashing and YARA rules, which can ascertain whether, or to what degree, two ransomware samples are similar to each other. However, the mechanisms of these three methods are quite different and their comparative assessment is difficult. Therefore, this paper presents an evaluation of these three methods for triaging the four most pertinent ransomware categories WannaCry, Locky, Cerber and CryptoWall. It evaluates their triaging performance and run-time system performance, highlighting the limitations of each method.

Naik, Nitin, Jenkins, Paul, Savage, Nick, Yang, Longzhi.  2019.  Cyberthreat Hunting - Part 2: Tracking Ransomware Threat Actors Using Fuzzy Hashing and Fuzzy C-Means Clustering. 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–6.

Threat actors are constantly seeking new attack surfaces, with ransomeware being one the most successful attack vectors that have been used for financial gain. This has been achieved through the dispersion of unlimited polymorphic samples of ransomware whilst those responsible evade detection and hide their identity. Nonetheless, every ransomware threat actor adopts some similar style or uses some common patterns in their malicious code writing, which can be significant evidence contributing to their identification. he first step in attempting to identify the source of the attack is to cluster a large number of ransomware samples based on very little or no information about the samples, accordingly, their traits and signatures can be analysed and identified. T herefore, this paper proposes an efficient fuzzy analysis approach to cluster ransomware samples based on the combination of two fuzzy techniques fuzzy hashing and fuzzy c-means (FCM) clustering. Unlike other clustering techniques, FCM can directly utilise similarity scores generated by a fuzzy hashing method and cluster them into similar groups without requiring additional transformational steps to obtain distance among objects for clustering. Thus, it reduces the computational overheads by utilising fuzzy similarity scores obtained at the time of initial triaging of whether the sample is known or unknown ransomware. The performance of the proposed fuzzy method is compared against k-means clustering and the two fuzzy hashing methods SSDEEP and SDHASH which are evaluated based on their FCM clustering results to understand how the similarity score affects the clustering results.

Rodriguez, Ariel, Okamura, Koji.  2019.  Generating Real Time Cyber Situational Awareness Information Through Social Media Data Mining. 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). 2:502–507.
With the rise of the internet many new data sources have emerged that can be used to help us gain insights into the cyber threat landscape and can allow us to better prepare for cyber attacks before they happen. With this in mind, we present an end to end real time cyber situational awareness system which aims to efficiently retrieve security relevant information from the social networking site This system classifies and aggregates the data retrieved and provides real time cyber situational awareness information based on sentiment analysis and data analytics techniques. This research will assist security analysts to evaluate the level of cyber risk in their organization and proactively take actions to plan and prepare for potential attacks before they happen as well as contribute to the field through a cybersecurity tweet dataset.
Ibarra, Jaime, Javed Butt, Usman, Do, Anh, Jahankhani, Hamid, Jamal, Arshad.  2019.  Ransomware Impact to SCADA Systems and its Scope to Critical Infrastructure. 2019 IEEE 12th International Conference on Global Security, Safety and Sustainability (ICGS3). :1–12.
SCADA systems are being constantly migrated to modern information and communication technologies (ICT) -based systems named cyber-physical systems. Unfortunately, this allows attackers to execute exploitation techniques into these architectures. In addition, ransomware insertion is nowadays the most popular attacking vector because it denies the availability of critical files and systems until attackers receive the demanded ransom. In this paper, it is analysed the risk impact of ransomware insertion into SCADA systems and it is suggested countermeasures addressed to the protection of SCADA systems and its components to reduce the impact of ransomware insertion.
Aidan, J. S., Zeenia, Garg, U..  2018.  Advanced Petya Ransomware and Mitigation Strategies. 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC). :23–28.

In this cyber era, the cyber threats have reached a new level of menace and maturity. One of the major threat in this cyber world nowadays is ransomware attack which had affected millions of computers. Ransomware locks the valuable data with often unbreakable encryption codes making it inaccessible for both organization and consumers, thus demanding heavy ransom to decrypt the data. In this paper, advanced and improved version of the Petya ransomware has been introduced which has a reduced anti-virus detection of 33% which actually was 71% with the original version. System behavior is also monitored during the attack and analysis of this behavior is performed and described. Along with the behavioral analysis two mitigation strategies have also been proposed to defend the systems from the ransomware attack. This multi-layered approach for the security of the system will minimize the rate of infection as cybercriminals continue to refine their tactics, making it difficult for the organization's complacent development.