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Entropy-Based Anomaly Detection in a Network

机译:网络中基于熵的异常检测

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摘要

Every computer on the Internet these days is a potential target for a new attack at any moment. In this paper we propose a method to enhance network security using entropy based anomaly detection. Intrusion detection system Snort is used for collecting the complete network traffic. Snort alert is then processed for selecting the attributes. Then Shannon entropies are calculated to analyze source IP address, source port address, destination IP address, destination port address, source IP threat, source port threat, destination IP threat, destination port threat and datagram length. Renyi cross entropy method is applied on Shannon entropy vector to detect network attack. After detecting attack in network, list of source IP address, source port address, destination IP address, destination port address with respective number of attack are generated for the advance protection of the network. This facilitates the network administrator to block/unblock IP addresses and ports where is attacks were detected. In this method about 90% attacks are detected. The rest 10% network traffic could not be detected. Since some low priority network traffic being treated as genuine traffic.
机译:这些天互联网上的每台电脑都是任何时刻新攻击的潜在目标。在本文中,我们提出了一种使用基于熵的异常检测来提高网络安全的方法。入侵检测系统Snort用于收集完整的网络流量。然后处理Snort警报以选择属性。然后计算Shannon Entropies以分析源IP地址,源端口地址,目标IP地址,目标端口地址,源IP威胁,源端口威胁,目的地IP威胁,目标端口威胁和数据报长度。瑞尼跨熵方法应用于香农熵向量以检测网络攻击。在网络中检测到攻击后,为网络的提前保护生成源IP地址,源端口地址,目标IP地址,目的端口地址,目的端口地址,用于网络的预先保护。这有助于网络管理员阻止/取消阻止IP地址和端口检测到攻击的端口。在这种方法中,检测到约90%的攻击。无法检测到其余10%的网络流量。由于一些低优先级网络流量被视为真正的流量。

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