Impact Analysis of Malware Based on Call Network API With Heuristic Detection Method

Zero-day Malware Detection based on Supervised Learning Algorithms of API call Signatures

Zero-day or unknown malware are created using code obfuscation techniques that can modify the parent code to produce offspring copies which have the same functionality but with different signatures. Current techniques reported in literature lack the capability of detecting zero-day malware with the required accuracy and efficiency. In this paper, we have proposed and evaluated a novel method of...

Android Malware Detection using Deep Learning on API Method Sequences

Android OS experiences a blazing popularity since the last few years. This predominant platform has established itself not only in the mobile world but also in the Internet of Things (IoT) devices. This popularity, however, comes at the expense of security, as it has become a tempting target of malicious apps. Hence, there is an increasing need for sophisticated, automatic, and portable malware...

Malware Characterization Using Windows API Call Sequences

Malware Detection Through Call Graphs

Each day, anti-virus companies receive large quantities of potentially harmful executables. Many of the malicious samples among these executables are variations of earlier encountered malware, created by their authors to evade pattern-based detection. Consequently, robust detection approaches are required, capable of recognizing similar samples automatically. In this thesis, malware detection t...

Improving the Detection of Malware Behaviour Using Simplified Data Dependent API Call Graph

Malware stands for malicious software. It is software that is designed with a harmful intent. A malware detector is a system that attempts to identify malware using Application Programming Interface (API) call graph technique and/or other techniques. Matching the API call graph using graph matching algorithm have NP-complete problem and is slow because of computational complexity .In this study...