An overview of neural networks use in anomaly Intrusion Detection Systems

Yusuf Sani, Ahmed Mohamedou, Khalid Ali, Anahita Farjamfar, Mohamed Azman, S. Shamsuddin
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引用次数: 26

Abstract

With the increasing number of computers being connected to the Internet, security of an information system has never been more urgent. Because no system can be absolutely secure, the timely and accurate detection of intrusions is necessary. This is the reason of an entire area of research, called Intrusion Detection Systems (IDS). Anomaly systems detect intrusions by searching for an abnormal system activity. But the main problem of anomaly detection IDS is that; it is very difficult to build, because of the difficulty in defining what is normal and what is abnormal. Neural network with its ability of learning has become one of the most promising techniques to solve this problem. This paper presents an overview of neural networks and their use in building anomaly intrusion systems.
神经网络在异常入侵检测系统中的应用综述
随着越来越多的计算机连接到互联网,信息系统的安全从未像现在这样紧迫。由于任何系统都不可能绝对安全,因此及时准确地检测入侵是必要的。这就是所谓入侵检测系统(IDS)的整个研究领域的原因。异常系统通过搜索异常的系统活动来检测入侵。但是异常检测IDS的主要问题是;它很难建立,因为很难定义什么是正常的,什么是不正常的。神经网络以其学习能力成为解决这一问题最有前途的技术之一。本文概述了神经网络及其在构建异常入侵系统中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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