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Identifying Peer-to-Peer Botnets Through Periodicity Behavior Analysis

机译:通过周期性行为分析识别对等僵尸网络

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摘要

Peer-to-Peer botnets have become one of the significant threat against network security due to their distributed properties. The decentralized nature makes their detection challenging. It is important to take measures to detect bots as soon as possible to minimize their harm. In this paper, we propose PeerGrep, a novel system capable of identifying P2P bots. PeerGrep starts from identifying hosts that are likely engaged in P2P communications, and then distinguishes P2P bots from P2P hosts by analyzing their active ratio, packet size and the periodicity of connection to destination IP addresses. The evaluation shows that PeerGrep can identify all P2P bots with quite low FPR even if the malicious P2P application and benign P2P application coexist within the same host or there is only one bot in the monitored network.
机译:对等僵尸网络由于其分布式属性而已成为严重威胁网络安全的威胁之一。分散的性质使其检测具有挑战性。重要的是要采取措施尽快检测机器人,以最大程度地减少它们的危害。在本文中,我们提出了PeerGrep,这是一种能够识别P2P机器人的新颖系统。 PeerGrep从识别可能参与P2P通信的主机开始,然后通过分析它们的活动率,数据包大小以及与目标IP地址的连接周期来区分P2P僵尸程序与P2P主机。评估显示,即使恶意P2P应用程序和良性P2P应用程序共存于同一主机中,或者受监控的网络中只有一个机器人,PeerGrep仍可以识别FPR较低的所有P2P机器人。

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