Related papers: DAEMON: Dataset-Agnostic Explainable Malware Class…

The rapid evolution of Android malware poses significant challenges to the maintenance and security of mobile applications (apps). Traditional detection techniques often struggle to keep pace with emerging malware variants that employ…

Our computer systems for decades have been threatened by various types of hardware and software attacks of which Malwares have been one of them. This malware has the ability to steal, destroy, contaminate, gain unintended access, or even…

It is well known that anti-malware scanners depend on malware signatures to identify malware. However, even minor modifications to malware code structure results in a change in the malware signature thus enabling the variant to evade…

In this study we have presented a novel feature representation for malicious programs that can be used for malware classification. We have shown how to construct the features in a bottom-up approach, and analyzed the overlap of malicious…

Identification of the family to which a malware specimen belongs is essential in understanding the behavior of the malware and developing mitigation strategies. Solutions proposed by prior work, however, are often not practicable due to the…

Machine-learning methods have already been exploited as useful tools for detecting malicious executable files. They leverage data retrieved from malware samples, such as header fields, instruction sequences, or even raw bytes, to learn…

Machine learning-based malware detection dominates current security defense approaches for Android apps. However, due to the evolution of Android platforms and malware, existing such techniques are widely limited by their need for constant…

The daily amount of Android malicious applications (apps) targeting the app repositories is increasing, and their number is overwhelming the process of fingerprinting. To address this issue, we propose an enhanced Cypider framework, a set…

Malicious activities in cyberspace have gone further than simply hacking machines and spreading viruses. It has become a challenge for a nations survival and hence has evolved to cyber warfare. Malware is a key component of cyber-crime, and…

Malware is one of the most common and severe cyber-attack today. Malware infects millions of devices and can perform several malicious activities including mining sensitive data, encrypting data, crippling system performance, and many more.…

It is well-known that malware constantly evolves so as to evade detection and this causes the entire malware population to be non-stationary. Contrary to this fact, prior works on machine learning based Android malware detection have…

Repackaging is a technique that has been increasingly adopted by authors of Android malware. The main problem facing the research community working on devising techniques to detect this breed of malware is the lack of ground truth that…

Malware family classification is a significant issue with public safety and research implications that has been hindered by the high cost of expert labels. The vast majority of corpora use noisy labeling approaches that obstruct definitive…

The escalating frequency and scale of recent malware attacks underscore the urgent need for swift and precise malware classification in the ever-evolving cybersecurity landscape. Key challenges include accurately categorizing closely…

The astonishing spread of Android OS, not only in smartphones and tablets but also in IoT devices, makes this operating system a very tempting target for malware threats. Indeed, the latter are expanding at a similar rate. In this respect,…

The rapid evolution of malware variants requires robust classification methods to enhance cybersecurity. While Large Language Models (LLMs) offer potential for generating malware descriptions to aid family classification, their utility is…

In applying deep learning for malware classification, it is crucial to account for the prevalence of malware evolution, which can cause trained classifiers to fail on drifted malware. Existing solutions to address concept drift use active…

Dynamic malware analysis executes the program in an isolated environment and monitors its run-time behaviour (e.g. system API calls) for malware detection. This technique has been proven to be effective against various code obfuscation…

The emergence of mobile platforms with increased storage and computing capabilities and the pervasive use of these platforms for sensitive applications such as online banking, e-commerce and the storage of sensitive information on these…

With the advent of new technologies, using various formats of digital gadgets is becoming widespread. In today's world, where everyday tasks are inevitable without technology, this extensive use of computers paves the way for malicious…