BOTNET DETECTION AND OPTIMIZATION USING HONEYPOT

Abstract

Botnet is the most extensive and thoughtful threat which follows commonly in today's cyberattacks. A botnet is a collection of bargained computers which are at all controlled by hackers to launch various network attacks, such as Botnet attack, spam, click fraud, identity theft and information phishing. Botnet has developed a popular and creative tool overdue many cyberattacks. The important characteristic of botnets is the use of command and control channels through which they can be efficient and absorbed. Lately malicious botnets change into HTTP botnets out of typical IRC botnets. This makes the detection of botnet command and control a stimulating problem. The Thesis then classifies and inspects problems common to many current packet sniffing applications, shows how these problems can effortlessly undermine the network administrator's intents and lead to a false intellect of security, and proposes solutions to these problems. Lastly, in this thesis accomplishes that botnet identification number follow by support vector classifier (SVM) that would distribute on the basis of header length such as h1, h2 and h3 although h3 would combine balance packet for h1 and h2 which currently a viable network security mechanism, but that its utility could be greatly improved with the extensions proposed in the Thesis.