Coarse grained botnet detection based on anomaly and community detection

Computer engineering

Botnets are the foremost common vehicle of cyber-criminal activity. They’re used for spamming, phishing, denial-of-service attacks, brute-force cracking, stealing non-public data, and cyber warfare. A botnet (also referred to as a zombie army) may be a range of net computers that, though their homeowners ar unaware of it, are got wind of to forward transmissions (including spam or viruses) to alternative computers on the web. In this paper, we have proposed a two-stage approach for botnet detection. the primary stage detects and collects network anomalies that are related to the presence of a botnet whereas the second stage identifies the bots by analyzing these anomalies. Our approach exploits the subsequent 2 observations: (1) botmasters or attack targets are easier to find as a result of the impact with several alternative nodes, and (2) the activities of infected machines are a lot of correlative with one another than those of traditional machines.

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