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Existing research on malware detection focuses almost exclusively on the detection rate. However, in some cases, it is also important to understand the results of our algorithm, or to obtain more information, such as where to investigate in…

ML-based malware detection on dynamic analysis reports is vulnerable to both evasion and spurious correlations. In this work, we investigate a specific ML architecture employed in the pipeline of a widely-known commercial antivirus company,…

Both malware and antivirus detection tools advance in their capabilities. Malware aim is to evade the detection while antivirus is to detect the malware. Over time, the detection techniques evolved from simple static signature matching over…

Adversarial Malware Generation (AMG), the generation of adversarial malware variants to strengthen Deep Learning (DL)-based malware detectors has emerged as a crucial tool in the development of proactive cyberdefense. However, the majority…

In this paper, we present a scientific evaluation of four prominent malware detection tools to assist an organization with two primary questions: To what extent do ML-based tools accurately classify previously- and never-before-seen files?…

Current malware detection and classification approaches generally rely on time consuming and knowledge intensive processes to extract patterns (signatures) and behaviors from malware, which are then used for identification. Moreover, these…

Malware detection has become a major concern due to the increasing number and complexity of malware. Traditional detection methods based on signatures and heuristics are used for malware detection, but unfortunately, they suffer from poor…

There has been a surge of interest in using machine learning (ML) to automatically detect malware through their dynamic behaviors. These approaches have achieved significant improvement in detection rates and lower false positive rates at…

In today's world, we are performing our maximum work through the Internet, i.e., online payment, data transfer, etc., per day. More than thousands of users are connecting. So, it's essential to provide security to the user. It is necessary…

This paper delves into the dynamic landscape of computer security, where malware poses a paramount threat. Our focus is a riveting exploration of the recent and promising hardware-based malware detection approaches. Leveraging hardware…

Deep learning has been used in the research of malware analysis. Most classification methods use either static analysis features or dynamic analysis features for malware family classification, and rarely combine them as classification…

Machine-learning models have been recently used for detecting malicious Android applications, reporting impressive performances on benchmark datasets, even when trained only on features statically extracted from the application, such as…

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…

Detection of malware cyber-attacks at the processor microarchitecture level has recently emerged as a promising solution to enhance the security of computer systems. Security mechanisms, such as hardware-based malware detection, use machine…

Existing malware detectors on safety-critical devices have difficulties in runtime detection due to the performance overhead. In this paper, we introduce PROPEDEUTICA, a framework for efficient and effective real-time malware detection,…

In the modern era, malware is experiencing a significant increase in both its variety and quantity, aligning with the widespread adoption of the digital world. This surge in malware has emerged as a critical challenge in the realm of…

In the digitized world, smartphones and their apps play an important role. To name just a few examples, some apps offer possibilities for entertainment, others for online banking, and others offer support for two-factor authentication.…

Machine Learning (ML) techniques can facilitate the automation of malicious software (malware for short) detection, but suffer from evasion attacks. Many studies counter such attacks in heuristic manners, lacking theoretical guarantees and…

Due to increasing threats from malicious software (malware) in both number and complexity, researchers have developed approaches to automatic detection and classification of malware, instead of analyzing methods for malware files manually…

Malwares are continuously growing in sophistication and numbers. Over the last decade, remarkable progress has been achieved in anti-malware mechanisms. However, several pressing issues (e.g., unknown malware samples detection) still need…