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A hybrid artificial immune system for IDS based on SVM and belief function

机译:基于SVM和置信函数的IDS混合人工免疫系统。

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Increased connectivity and the use of the internet have exposed the subversion in front of the organizations, there for there is a need to use of intrusion detection system to protect information system and communication network from malicious attacks and unauthorized access. An intrusion detection system (IDS) is a security system that monitors computer systems and network traffic, analyze that traffic to identify possible security breaches and raise alerts. An IDS triggers thousands of alerts per day which is difficult for human users to analyze them and take appropriate actions. It is important to reduce the false alarm alerts, intelligently integrate and correlate them in order to present a high level view of the detected security issue to the administrator. In this paper an hybrid model has been proposed in which intrusion detection takes place with the help Dendritic Cell Algorithm and Dempester belief theory along with SVM classification algorithms. It made the Intrusion Detection System much more efficient and accurate as compared to the existing System. It also seems to be an improvement in precise value of proper alarm generation to enhance the performance of the system.
机译:连接性的提高和Internet的使用已经暴露了组织的颠覆性,因此需要使用入侵检测系统来保护信息系统和通信网络免受恶意攻击和未经授权的访问。入侵检测系统(IDS)是一种安全系统,可以监视计算机系统和网络流量,分析该流量以识别可能的安全漏洞并发出警报。 IDS每天会触发数千个警报,这对于人类用户来说很难分析警报并采取适当的措施。重要的是减少虚假警报,智能地集成和关联虚假警报,以便向管理员提供检测到的安全问题的高级视图。本文提出了一种混合模型,其中借助树突状细胞算法和Dempester信念理论以及SVM分类算法进行入侵检测。与现有系统相比,它使入侵检测系统更加高效和准确。这似乎是对适当警报生成的精确值的一种改进,以增强系统的性能。

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