期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2017
卷号：5
期号：11
页码：16381
DOI：10.15680/IJIRCCE.2017.0511001
出版社：S&S Publications
摘要：Over the years, there has been rapid advancement in internet technologies, such as the email, the worldwide web, VOIP, social networks, etc. Networks of compromised individual and corporate computers called (botnets)have been used to deploy malware, such as Viruses, Worms, Trojans, Spyware etc, to vulnerable computerson a globalscale. Botnets are used for various kinds of malicious activities on the internet including: distributed denial of service(DDOS) attacks, massive spam email messages, distributing other malware, click fraud attack and information theft,etc. Better Security decisions are usually associated with experience in cyber security, advanced-technologies, and richdata and information, as such an earnest and determined collaborative approach to botnet detection is likely to have asignificant positive outcome in tackling the menace of botnets. In this paper, we propose a novel botnet detectionapproach that leverages the expertise and experience of several research collaborators, as well as the abundant data andinformation at each collaborator’s disposal, to detect botnets irrespective of command and control protocol, type ofarchitecture, or infection behaviour. We use Python scripts to broadcast diagnosis request to peer collaborators and thenuse Supervised Machine learning to learn through False Positives (), False Negatives (), True Positives (), andTrue Negatives (), the detection accuracy of peer collaborators, detect malicious collaborators, and finally, detectcostly and unreliable collaborators.
