Context: In last decade, due to tremendous usage of smart phones it seems that these
gadgets became an essential necessity of day-to-day life. People are using new …

Pervasive growth and usage of the Internet and mobile applications have expanded
cyberspace. The cyberspace has become more vulnerable to automated and prolonged …

In this paper, we argue that machine learning techniques are not ready for malware
detection in the wild. Given the current trend in malware development and the increase of …

Mobile ransomware is on the rise and effective defense from it is of utmost importance to
guarantee security of mobile users' data. Current solutions provided by antimalware vendors …

In response to the volume and sophistication of malicious software or malware, security
investigators rely on dynamic analysis for malware detection to thwart obfuscation and …

For efficient malware detection, there are more and more deep learning methods based on
raw software binaries. Recent studies show that deep learning models can easily be fooled …

This paper presents BRIDEMAID, a framework which exploits an approach static and
dynamic for accurate detection of Android malware. The static analysis is based on n-grams …

IoT security and privacy has raised grave concerns. Efforts have been made to design tools
to identify and understand vulnerabilities of IoT systems. Most of the existing protocol …

The rapid growth and popularity of large language model (LLM) app stores have created
new opportunities and challenges for researchers, developers, users, and app store …

Android system has become a target for malware developers due to its huge market globally
in recent years. The emergence of 5G in the market and limited protocols post a great …