The evolution of recent malicious software with the rising use of digital services has
increased the probability of corrupting data, stealing information, or other cybercrimes by …

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 …

Advances in machine learning (ML) in recent years have enabled a dizzying array of
applications such as data analytics, autonomous systems, and security diagnostics. ML is …

Advances in machine learning (ML) in recent years have enabled a dizzying array of
applications such as data analytics, autonomous systems, and security diagnostics. ML is …

The widespread adoption of Android devices and their capability to access significant
private and confidential information have resulted in these devices being targeted by …

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 …

Large language models (LLMs) have demonstrated significant potential in the realm of
natural language understanding and programming code processing tasks. Their capacity to …

The explosive growth of Internet and the recent increasing trends in automation using
intelligent applications have provided a veritable playground for malicious software …

Modern malware is designed with mutation characteristics, namely polymorphism and
metamorphism, which causes an enormous growth in the number of variants of malware …

Monitoring Indicators of Compromise (IOC) leads to malware detection for identifying
malicious activity. Malicious activities potentially lead to a system breach or data …