FILE-LESS MALWARE DETECTION

Abstract

Today, Everything is present digitally on our computer system and every organisation uses the computer for its daily work, Nearly 50 billion devices are currently connected to the Internet. Every device which is connected to the internet is vulnerable to cyberattack, to protect them from any attack multiple techniques are introduced like, Anomaly-based detection, Specification-based detection and Signature-based detection but with the evolution, in cybersecurity measures, the threat has also evolved with time, especially in the field of malware. Typically, malware is based on the file system which can be detected by the antivirus software. To overcome this file-less malware is developed by the attackers which do not use any file system, so it bypasses any signature-based detection. File-less malware can be dangerous for any organisation because of its persistence to over come from the danger of file-less malware few method are developed like, Detection on the basis of system behaviour, detection on the basis of rules and detection on the basis of attack. To make the computer system secure continuous analysis of the malware is necessary, So that malware can be detected easily. This project uses 4 different machine learning algorithms i.e Logistic Regression, K Neared Neighbour, Decision Tree and Support Vector Machine all the algorithm comes under supervised learning and are capable of detecting any type of labeled value. Our dataset contains 10 different file-less malware and we have applied the all the algorithm in it for the detection part.