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Self Adaptive Intrusion Detection Technique Using Data Mining Concept in an Ad-hoc Network

机译:Ad-hoc网络中基于数据挖掘概念的自适应入侵检测技术

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The Intrusion Detection System mainly adopted from the traditional Wired Network or Distributed Network cannot provide a satisfying solution to the increasing security threats in an Ad-Hoc Network. Adhoc-Network due to its typical infrastructure less technical background, implementing an effective security solution for it is extremely challenging. Moreover due to its vulnerable nature attackers consistently try new attack mechanism which generally goes undetected for the system that uses pattern matching to detect traces os intrusion behavior in the incoming data. The paper aims at providing IDS based on self learning technique where the system when comes across an unknown data pattern classifies it as an attack or non attack after comparing and considering its variation from an attack free scenario.
机译:主要从传统的有线网络或分布式网络采用的入侵检测系统不能为Ad-Hoc网络中日益增加的安全威胁提供令人满意的解决方案。 Adhoc-Network由于其典型的基础架构较少的技术背景,因此为其实施有效的安全解决方案具有极大的挑战性。此外,由于其易受攻击的性质,攻击者一直在尝试新的攻击机制,而对于使用模式匹配来检测传入数据中的痕迹或入侵行为的系统,攻击机制通常是无法检测到的。本文旨在提供一种基于自学习技术的IDS,其中在比较并考虑其与无攻击方案的差异之后,系统在遇到未知数据模式时会将其分类为攻击或非攻击。

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