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A Neural Network Model for Detection Systems Based on Data Mining and False Errors

机译:基于数据挖掘和错误错误的检测系统神经网络模型

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Nowadays, computer network systems play an increasingly important role in our society. They have become the target of a wide array of malicious attacks that can turn into actual intrusions. This is the reason why computer security has become an essential concern for network administrators. Intrusions can wreak havoc on LANs. And the time and cost to repair the damage can grow to extreme proportions. Instead of using passive measures to fix and patch security holes, it is more effective to adopt proactive measures against intrusions. Recently, several IDS have been proposed and they are based on various technologies. However, these techniques, which have been used in many systems, are useful only for detecting the existing patterns of intrusion. It can not detect new patterns of intrusion. Therefore, it is necessary to develop a new technology of IDS that can find new patterns of intrusion. This paper investigates the asymmetric costs of false errors to enhance the detection systems performance.
机译:如今,计算机网络系统在我们的社会中发挥着越来越重要的作用。他们已成为一种可以变成实际入侵的广泛恶意攻击的目标。这就是为什么计算机安全成为网络管理员必须关注的原因。侵入可能会在兰斯造成严重破坏。修复损坏的时间和成本可以增长到极端比例。采用对入侵的积极措施更有效,而不是使用被动措施来修复和修补安全漏洞。最近,已经提出了几个ID,他们基于各种技术。然而,这些技术已经在许多系统中使用,仅用于检测现有的入侵模式。它无法检测到新的入侵模式。因此,有必要开发一种可以找到新的入侵模式的ID技术。本文调查了虚假误差的不对称成本,以提高检测系统性能。

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