Related papers: RanStop: A Hardware-assisted Runtime Crypto-Ransom…

Ransomware has appeared as one of the major global threats in recent days. The alarming increasing rate of ransomware attacks and new ransomware variants intrigue the researchers to constantly examine the distinguishing traits of ransomware…

Ransomware, a class of self-propagating malware that uses encryption to hold the victims' data ransom, has emerged in recent years as one of the most dangerous cyber threats, with widespread damage; e.g., zero-day ransomware WannaCry has…

Researchers have proposed a wide range of ransomware detection and analysis schemes. However, most of these efforts have focused on older families targeting Windows 7/8 systems. Hence there is a critical need to develop efficient solutions…

Crypto-Ransomware has been increasing in sophistication since it first appeared in September 2013, leveraging new attack vectors, incorporating advanced encryption algorithms, and expanding the number of file types it targets. In this…

In the past decade, the cyber-crime related to mobile devices has increased. Mobile devices, especially the ones running on Android operating system are particularly interesting to malware creators, as the users often keep the biggest…

Crypto-ransomware has caused an unprecedented scope of impact in recent years with an evolving level of sophistication. An extensive range of studies have been on defending against ransomware and reviewing the efficacy of various…

The increasing sophistication of cyber threats has necessitated the development of advanced detection mechanisms capable of identifying malicious activities with high precision and efficiency. A novel approach, termed Autonomous Feature…

Ransomware, a fearsome and rapidly evolving cybersecurity threat, continues to inflict severe consequences on individuals and organizations worldwide. Traditional detection methods, reliant on static signatures and application behavioral…

Threats from the internet, particularly malicious software (i.e., malware) often use cryptographic algorithms to disguise their actions and even to take control of a victim's system (as in the case of ransomware). Malware and other threats…

Encryption-based cyber threats continue to evolve, employing increasingly sophisticated techniques to bypass traditional detection mechanisms. Many existing classification strategies depend on static rule sets, signature-based matching, or…

The spread of ransomware continues to cause devastation and is a major concern for the security community. An often-used technique against this threat is the use of honey (or canary) files, which serve as ``trip wires'' to detect ransomware…

Recent works have shown promise in using microarchitectural execution patterns to detect malware programs. These detectors belong to a class of detectors known as signature-based detectors as they catch malware by comparing a program's…

Cybercriminals have been exploiting cryptocurrencies to commit various unique financial frauds. Covert cryptomining - which is defined as an unauthorized harnessing of victims' computational resources to mine cryptocurrencies - is one of…

The rise in frequency and complexity of malware attacks are viewed as a major threat to modern digital infrastructure, which means that traditional signature-based detection methods are becoming less effective. As cyber threats continue to…

Ransomware core capability, unauthorized encryption, demands controls that identify and block malicious cryptographic activity without disrupting legitimate use. We present a probabilistic, risk-based access control architecture that…

Existing anti-malware software and reverse engineering toolkits struggle with stealthy sub-OS rootkits due to limitations of run-time kernel-level monitoring. A malicious kernel-level driver can bypass OS-level anti-virus mechanisms easily.…

Ransomware defense solutions that can quickly detect and classify different ransomware classes to formulate rapid response plans have been in high demand in recent years. Though the applicability of adopting deep learning techniques to…

The aim of this study is to propose and evaluate an advanced ransomware detection and classification method that combines a Stacked Autoencoder (SAE) for precise feature selection with a Long Short Term Memory (LSTM) classifier to enhance…

Machine learning based malware detection techniques rely on grayscale images of malware and tends to classify malware based on the distribution of textures in graycale images. Albeit the advancement and promising results shown by machine…

Blockchain networks are critical for safeguarding digital transactions and assets, but they are increasingly targeted by ransomware attacks exploiting zero-day vulnerabilities. Traditional detection techniques struggle due to the complexity…