基于协同运动的移动自组网入侵检测

K. Pazhanisamy, L. Parthiban
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引用次数: 0

摘要

随着无线设备数量的持续快速增长,移动自组织网络(MANET)已经成为一项令人兴奋和重大的技术进步。由于其开放的介质、不断变化的网络设计、合作机制、缺乏保护措施和管理点、缺乏连贯的攻击层等特点,使其容易受到攻击。但是,正常功能经常会产生与“签名攻击”相对应的流量,从而导致错误警报。一个明显的缺点是在没有建立签名的情况下无法识别新的攻击。在本文中,我们描述了我们为创建MANET入侵检测(ID)功能所做的努力。基于我们之前在异常点检测方面的工作,我们探讨了部分群优化入侵检测(IDPSO)和支持向量回归(SVR)如何改进异常检测方法,以提供有关攻击类型和来源的额外信息。每当检测到异常时,我们可以使用一个基本公式来确定许多众所周知的攻击的攻击类型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mobile Ad Hoc Networks Intrusion Detection in Co-Operative Motion
As the number of wireless devices continues to increase rapidly, mobile ad hoc networking (MANET) has emerged as an exciting and significant technological advance. MANETs were susceptible to attacks because of their open media, continuously changing network design, cooperation mechanisms, lack of a protective measure and management point, and a coherent layer of attack. However, regular functioning frequently generates traffic corresponding to a "signature attack," which leads to false alerts. One of the significant disadvantages is the inability to identify new attacks without established signatures. In this article, we describe our efforts towards creating the capability for MANET intrusion detection (ID). Based on our previous works on outlier detection, we explore how Intrusion Detection in Partial Swarm Optimization (IDPSO) and Support vector Regression(SVR) may improve an anomaly detection method to give additional information about attack kinds and origins. We can use a basic formula to determine the attack type for many well-known assaults whenever an anomaly is detected.
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来源期刊
Alinteri Journal of Agriculture Sciences
Alinteri Journal of Agriculture Sciences AGRICULTURE, MULTIDISCIPLINARY-
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