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Entropy deviation method for analyzing network intrusion

机译:熵偏差法分析网络入侵

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Intrusion Detection plays a vital role in networks. In this paper, we propose a new method of finding out network anomalies using network behavior analysis based on network entropy. The proposed model works towards the determination of malicious IP addresses requesting services from the server. The Proposed Entropy Deviation method(EDM) has been implemented by capturing real time data, a resultant file is generated containing the list of IP addresses along with their entropy deviation of the attackers' machine. The model concludes that low entropy value determines that the user shows a normal behavior and a huge deviation from the regular entropy value determines the anomaly. The proposed EDM has great potential to embellish in this field by remodeling the reliability of networks as it helps preventing attacks.
机译:入侵检测在网络中起着至关重要的作用。本文提出了一种基于网络熵的网络行为分析发现网络异常的新方法。提出的模型可用于确定从服务器请求服务的恶意IP地址。提议的熵偏差方法(EDM)通过捕获实时数据来实现,生成的结果文件包含IP地址列表以及攻击者机器的熵偏差。该模型得出的结论是,低熵值确定用户显示正常行为,而与常规熵值的巨大偏差确定异常。拟议的EDM具有重塑网络可靠性的潜力,因为它可以帮助防止攻击,因此可以通过重新建模网络的可靠性来加以修饰。

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