A new method for behavioural-based malware detection using reinforcement learning. (2018)

• Record Type: Journal Article
• Authors: Mohammadkhani, Sepideh; Esmaeilpour, Mansour
• Is Part Of: International journal of data mining, modelling and management. Volume 10:Number 4(2018)
• Page Start: 314
• Page End: 330
• Publication Date: 2018
• Subjects: antivirus -- AVS -- malware -- reinforcement learning; Data mining -- Periodicals; Information science -- Periodicals; Databases -- Periodicals; 005.7
• Languages: English
• ISSNs: 1759-1163

Abstract

Malware is - the abbreviation for malicious software - a comprehensive term for software that is deliberately created to perform an unauthorised and often harmful function. Viruses, backdoors, key-loggers, Trojans, password thieves' software, spyware, adwares are number of malware samples. Previously, calling something a virus or Trojan was enough. However, methods of contamination are developed, the term virus and other malware definition was not satisfactory for all types of malicious programs. This research focus on clustering the malware according to the malware features. To avoid the dangers of malware, some applications have been created to track them down. This paper presents a new method for detection of malware using reinforcement learning. The result demonstrates that the proposed method can detect the malware more accurate.