A Reliable & Scalable Frame Work for HTTP BotNet Detection
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2017 by IJCTT Journal|
|Year of Publication : 2017|
|Authors : Dr.R.Kannan, Mrs.Poongodi|
|DOI : 10.14445/22312803/IJCTT-V54P105|
Dr.R.Kannan, Mrs.Poongodi "A Reliable & Scalable Frame Work for HTTP BotNet Detection". International Journal of Computer Trends and Technology (IJCTT) V53(1):19-23, December 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
With growing number of internet based applications, smart phones and mobile computing devices to connect such applications has increase the use of internet technology. The growing network capabilities enable distributing and resources sharing even among cross platform devices. Parallel processing enables to utilize resources exceeding more than one machine. Exploiting such development’s and computational freedom, attacker’s use botnets for various tasks including data stealing, denial of service, and other illegal activities. To detect botnets from regular activity in the network involves distinguishing regular traffic and botnet activity. The resilient nature of botnets can’t be predicted with regular time intervals and the activity may resume at any given time. This paper aims to classify the patterns of the botnets and to mitigate the effects of Botnet through detection and prevention framework proposed. The framework classifies the malicious activity using information mining methods to distinguish internet traffic from malicious traffic, once the traffic patterns are identified; future patters can be identified to remove botnets from the network.
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Botnet,SVMhyperplane,Clustering,BDoS,IRC and C&C.