Abstract
Abstract— A botnet is a malware that degrades the functionality as well as access to a healthy computer system through malware programs. Botnet programs perform DDoS attack, Spam, phishing attacks. Botnet attack takes place in two ways which are peer to peer attacks and command and control attack. The peer-to-peer attack takes place to by passing botnet attacks from one system to another in a peer-to-peer network while the command-and-control attack takes place by a botmaster attack on a server which uses various transactions in exchange with systems on the network and those nodes in the networks function as slaves. The report presents a survey of various techniques of botnet detection models built using several types of machine learning techniques. The report gives the review on various methodologies involved in Botnet Detection and to identify the best methods involved to understand various dataset. We also surveyed on how classification, clustering is used in detection of Botnet to improve the accuracy of the model.
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