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Classification Denial Of Service (Dos) Attack Using Artificial Neural Network Learning Vector Quantization (Lvq)

机译:使用人工神经网络学习矢量量化(Lvq)的分类拒绝服务(Dos)攻击

摘要

Network security is an important aspect in computer network defense. There are many threats find vulnerabilities and exploits for launching attacks. Threats that purpose to prevent users get the service of the system is Denial of Service (DoS). One of software application that can detect intrusion on is an Intrusion Detection System (IDS). IDS is a defense system to detect suspicious activity on the network. IDS has ability to categorize the various types of attack and not attack. In this research, Learning Vector Quantization (LVQ) neural network is used to classify the type of attacks. LVQ is a method to study the competitive supervised layer. If two input vectors approximately equal, then the competitive layers will put both the input vector into the same class. The results show IDS able to classify PING and UDP Floods are 100%.
机译:网络安全是计算机网络防御中的重要方面。有许多威胁可以发现漏洞和漏洞来发起攻击。旨在阻止用户获得系统服务的威胁是拒绝服务(DoS)。可以检测入侵的软件应用程序之一就是入侵检测系统(IDS)。 IDS是一种防御系统,用于检测网络上的可疑​​活动。 IDS能够对各种类型的攻击进行分类,而不是对攻击进行分类。在这项研究中,学习矢量量化(LVQ)神经网络用于对攻击类型进行分类。 LVQ是研究竞争监督层的一种方法。如果两个输入向量近似相等,则竞争层会将两个输入向量归为同一类。结果表明,能够对PING和UDP Flood进行分类的IDS为100%。

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