Robust Analysis of XXE Attack Produced by Malware

Abstract

Malware Analysis is one of the major growing sections in the cyber security area. Various trends and types have been introduced in the industry for example static malware analysis, Dynamic malware analysis, hybrid malware analysis and machine learning-based malware analysis techniques. There is various malware introduced for example virus, Worms, ransomware, spyware, botnets, etc. Security threats have increased drastically over the period. From viruses, spyware, worms, trojans, and ransomware to many zero-day Malware is reported and exploited in different platforms. Platforms like Windows, Android, and Cloud (Iaas or Paas). The Phenomenon is like attackers always making targets to humans via social engineering methodology or Phishing. When we talk about humans, the first thing that comes to mind of an attacker is the platform from which they will be able to concentrate on the target. The basic approach used mainly in detecting Malware in any platform is signature-based detection, which is quite beneficial. Still, as Malware is designed to be more obfuscated, detecting those malicious activities using a signature-based approach takes a lot of work. After the signature-based method, the behavior-based process is used to detect Malware. As some drawbacks appeared in both approaches, then, researchers found methodologies that can use Machine Learning Algorithms, for example, KNN, Random Forest, Nearest Neighbor, etc.