Malware developers continually attempt to modify the execution pattern of malicious code
hiding it inside apparent normal applications, which makes its detection and classification …

In this work, we propose SigMal, a fast and precise malware detection framework based on
signal processing techniques. SigMal is designed to operate with systems that process large …

Machine learning classifiers are a vital component of modern malware and intrusion
detection systems. However, past studies have shown that classifier based detection …

Recent studies have demonstrated the effectiveness of Hardware Performance Counters
(HPCs) for detecting pattern of malicious applications. Hardware-supported detectors utilize …

Although malicious software (malware) has been around since the early days of computers,
the sophistication and innovation of malware has increased over the years. In particular, the …

Malicious software deliberately affects the computer systems. Malware are analyzed using
static or dynamic analysis techniques. Using these techniques, unique patterns are …

In the Internet age, malware (such as viruses, trojans, ransomware, and bots) has posed
serious and evolving security threats to Internet users. To protect legitimate users from these …

Applying machine learning techniques to malware detection is a common approach to try to
overcome the limitations of signature-based methods. However, it is difficult to engineer a …

In this paper, we consider the relevance of timeline in the construction of datasets, to
highlight its impact on the performance of a machine learning-based malware detection …

In this research, we compare malware detection techniques based on static, dynamic, and
hybrid analysis. Specifically, we train Hidden Markov Models (HMMs) on both static and …