A REVIEW OF PEER-TO-PEER BOTNET DETECTION TECHNIQUES
نویسندگان
چکیده
منابع مشابه
A Review of Peer-to-Peer Botnet Detection Techniques
In recent years, Peer-to-Peer technology has an extensive use. Botnets have exploited this technology efficiently and introduced the P2P botnet, which uses P2P network for remote control of its bots and become one of the most significant threats to computer networks. They are used to make DDOS attacks, generate spam, click fraud and steal sensitive information. Compared with traditional botnets...
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-In this upcoming highly engaged traffic calls in the Peer to Peer (P2P) botnets the high scalability of detection systems is going down. Where the botmasters adopt modern system to detect traffic which ends in the malicious activities and poor scalability. So in this paper, we proposed a highly scalable botnet detection system for a stealthy peer to peer botnets. In this p2p detection it will ...
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. Abstract . . Traditional botnets use a centralized communications architecture where all the bots connect to Command and Control (C&C) servers. These servers are the weak point of the botnet, as they are easy targets for take down and monitoring. Peer-to-peer (p2p) botnets have a distributed architecture, which make them more resilient. This research aims at the detection of individual p2p bo...
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Peer-to-Peer botnets are legally taken by botmasters for the quick recovery against taking down efforts of the system. But it’s a harder one for the botmasters, because modern botnets are hidden and performing malicious activities it makes the process inefficient. Additionally because of sudden growth of the network traffic there was an ability to enlarge the malicious activities of the system....
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Botnets are becoming the predominant threat on the Internet today and is the primary vector for carrying out attacks against organizations and individuals. Botnets have been used in a variety of cybercrime, from click-fraud to DDOS attacks to the generation of spam. In this paper we propose an approach to detect botnet activity by classifying network traffic behavior using machine learning clas...
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ژورنال
عنوان ژورنال: Journal of Computer Science
سال: 2014
ISSN: 1549-3636
DOI: 10.3844/jcssp.2014.169.177