Paper Details

Botnet is the most frightful and malicious threats that usually occurs in the current cyber security. It can bedefined as collection of compromised computers that are controlled remotely by hackers or botma ster. Maliciousactivities of botnet such as launching DDoS (distributed denial of service)attacks, sending spam, Trojan, Phishingemails, information harvesting and click fraud. Recently malicious botnets progresses into HTTP botnets out of typicalIRC and P2P botnets. The defining characteristics of botnet are the use of command and control channel through whichthey can be updated and directed. Data mining techniques and algorithms allow us to automate detecting characteristicsfrom large amount of data, which the conventional heuristics and signature based methods could not apply. One majordata mining technique for extracting valuable pattern of the botnet attack is FP-growth algorithm. It aims to design andimplement mechanism to detect bot activity. After perform proposed approach we can discovers regularities andirregularities in large amount of data set.

Binal Panchal, Mitesh Thakore, Hardik Upadhyay, “Frequent Pattern Reorganization Approach to HTTP Botnet Detection”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 2, Issue 5, pp. 1263-1270, May 2015.