With the continuous development of technology, the types of malware and their variants
continue to increase, which has become an enormous challenge to network security. These …

Deep learning has been widely used for Android malware family recognition, but current
deep learning-based approaches make the closed-world assumption that malware families …

Ransomware has affected a broad range of public and private-sector organizations, and the
impacts include direct and indirect financial loss (eg, opportunity costs), reputational …

Current methods for classifying IoT malware predominantly utilize binary and family
classifications. However, these outcomes lack the detailed granularity to describe malicious …

Classifying malware images is important in cybersecurity. The nature of different families
and classes of malware images is imbalanced, which leads to a challenging issue of biased …

Develo** and evaluating malware classification pipelines to reflect real-world needs is as
vital to protect users as it is hard to achieve. In many cases, the experimental conditions …

Network attacks pose serious threats to cybersecurity. Researchers provide well-known
malicious sample datasets for evaluating methods to detect these attacks. However, we …

Our society is rapidly moving towards the digital age, which has led to a sharp increase in
IoT networks and devices. This growth requires more network security professionals, who …

The popularity of the Android operating system has itself become a reason for privacy
concerns. To deal with such malware threats, researchers have proposed various detection …

Mobile devices, particularly those employing the Android operating system, have a profound
influence on the quotidian behaviors of individuals across the globe. This dissertation offers …