Classification of Malware in HTTPs Traffic Using Machine Learning Approach

Authors : Abhay SİNGH

Pages : 644-655

Doi:10.31202/ecjse.990318

View : 16 | Download : 16

Publication Date : 2022-05-31

Article Type : Research Paper

Abstract :Cybersecurity and cyberwar have become crucial for a world with the continuous development and expansion of digitalization. In the current digital era, malware has become a significant threat for internet users. Malware spreads faster and poses a big threat to our computer safety. Hence, network security measures have an important role to play for neutralizing these cyber threats. In our research study, we collected some malicious and self-generated benign PCAP’s and then applied a suitable machine learning classification algorithm to build a traffic classifier. The proposed classifier classifies the malicious HTTPs traffic. The experimental results show the average accuracy insert ignore into journalissuearticles values(90%); and false-positive insert ignore into journalissuearticles values(0.030); for Random Forest insert ignore into journalissuearticles values(RF); classifier.
Keywords : Network traffic, Classification, HTTPs, Malware, Wireshark, Machine learning