入侵检测系统中各种机器学习方法综述

A. Gupta, Jitendra Agrawal
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引用次数: 12

摘要

随着技术的进步,现在的网络攻击越来越复杂,任何入侵检测系统都不容易检测到。由于大多数用户将他们的私人和敏感信息存储在计算机或任何其他数字媒体中,因此为这些计算机提供安全保护以防止攻击者是每个用户的基本要求。在过去的几十年里,人们提出了许多入侵检测系统。这些入侵检测主要分为基于签名的入侵检测系统和基于异常的入侵检测系统两种。本文的主要目的是比较现有的各种入侵检测系统及其优缺点。本文还将讨论用于检测入侵的各种机器学习方法和数据集。本文还将讨论使IDS设计更具挑战性的各种挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Comprehensive Survey on Various Machine Learning Methods used for Intrusion Detection System
With the advance in technology, now a day’s cyber-attack is more sophisticated which is not easily detected by the any intrusion detection system (IDS). Since most of the user store their private and sensitive information into the computer or any other digital media so providing security to these computers from the attacker is the essential requirement of each user. As number of intrusion detection system have been proposed in the last few decades. These IDS are mainly classified in two different types named signature based intrusion detection system and anomaly based intrusion detection system. The main objective of this paper is to compare various existing IDS with their strength and weakness. This paper will also discuss various machine learning approach and data sets which are used to detect intrusion. This paper will also discuss various challenges which makes IDS design more challenging.
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