Biblio

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%.

This paper presents TrustSign, a novel, trusted automatic malware signature generation method based on high-level deep features transferred from a VGG-19 neural network model pre-trained on the ImageNet dataset. While traditional automatic malware signature generation techniques rely on static or dynamic analysis of the malware's executable, our method overcomes the limitations associated with these techniques by producing signatures based on the presence of the malicious process in the volatile memory. Signatures generated using TrustSign well represent the real malware behavior during runtime. By leveraging the cloud's virtualization technology, TrustSign analyzes the malicious process in a trusted manner, since the malware is unaware and cannot interfere with the inspection procedure. Additionally, by removing the dependency on the malware's executable, our method is capable of signing fileless malware. Thus, we focus our research on in-browser cryptojacking attacks, which current antivirus solutions have difficulty to detect. However, TrustSign is not limited to cryptojacking attacks, as our evaluation included various ransomware samples. TrustSign's signature generation process does not require feature engineering or any additional model training, and it is done in a completely unsupervised manner, obviating the need for a human expert. Therefore, our method has the advantage of dramatically reducing signature generation and distribution time. The results of our experimental evaluation demonstrate TrustSign's ability to generate signatures invariant to the process state over time. By using the signatures generated by TrustSign as input for various supervised classifiers, we achieved 99.5% classification accuracy.

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.

Ransomware is one of the most increasing malwares used by cyber-criminals in recent days. This type of malware uses cryptographic technology that encrypts a user's important files, folders makes the computer systems unusable, holds the decryption key and asks for the ransom from the victims for recovery. The recent ransomware families are very sophisticated and difficult to analyze & detect using static features only. On the other hand, latest crypto-ransomwares having sandboxing and IDS evading capabilities. So obviously, static or dynamic analysis of the ransomware alone cannot provide better solution. In this paper, we will present a Machine Learning based approach which will use integrated method, a combination of static and dynamic analysis to detect ransomware. The experimental test samples were taken from almost all ransomware families including the most recent ``WannaCry''. The results also suggest that combined analysis can detect ransomware with better accuracy compared to individual analysis approach. Since ransomware samples show some ``run-time'' and ``static code'' features, it also helps for the early detection of new and similar ransomware variants.