Efficient Detection of Attacks in SIP Based VoIP Networks Using Linear l1-SVM Classifier
作者: Waleed NazihYasser HifnyWail ElkilaniTamer AbdelkaderHossam Faheem
刊名: International Journal of Computers Communications & Control, 2019, Vol.14 (4), pp.518-529
来源数据库: Agora University
DOI: 10.15837/ijccc.2019.4.3563
关键词: Machine learningSupport Vector Machines (SVMs)Session Initiation Protocol (SIP)VoIP attacks
原始语种摘要: The Session Initiation Protocol (SIP) is one of the most common protocols that are used for signaling function in Voice over IP (VoIP) networks. The SIP protocol is very popular because of its flexibility, simplicity, and easy implementation, so it is a target of many attacks. In this paper, we propose a new system to detect the Denial of Service (DoS) attacks (i.e. malformed message and invite flooding) and Spam over Internet Telephony (SPIT) attack in the SIP based VoIP networks using a linear Support Vector Machine with l1 regularization (i.e. l1-SVM) classifier. In our approach, we project the SIP messages into a very high dimensional space using string based n-gram features. Hence, a linear classifier is trained on the top of these features. Our experimental results show that the...
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  • SVM 共享虚拟存储器
  • SIP SAGE Improvement Program
  • regularization 正则化
  • propose 提议
  • features 特征
  • message 报文
  • classifier 分级机
  • Protocol 草案
  • simplicity 简单
  • popular 普及