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  • 标题:Hybridized Intrusion Detection System using Genetic and Tabu Search Algorithm
  • 本地全文:下载
  • 作者:Oluwakemi Christiana Abikoye ; Taye Oladele Aro ; Racheal Oyeranti Obisesan
  • 期刊名称:Annals. Computer Science Series
  • 印刷版ISSN:1583-7165
  • 电子版ISSN:2065-7471
  • 出版年度:2017
  • 卷号:15
  • 期号:1
  • 页码:139-150
  • 出版社:Mirton Publishing House, Timisoara
  • 摘要:

    As transactions, data communication and information systems are drastically increasing in the society, so many people are connected through internet for e-commerce and other electronic activities. The introduction of internet technology in business brings about great relief in reaching the end users. Also this technology invites numerous security threats of misuses and intrusions. Intrusion detection systems are significant element for network security infrastructure which plays key role in the detection of several attacks along the network. They are several techniques being employed in intrusion detection, but these methods are not completely flawless. In quest for an efficient Intrusion Detection System (IDS), this study employs hybridization technique which involves the Genetic Algorithm and Tabu-search to produce a robust Intrusion Detection System. Evaluation of the system on KKD 99 intrusion database, shows that the performance of proposed hybridized IDS is better than that of Genetic algorithm or tabu search method alone which can significantly detect almost all anomaly data in the computer network.

  • 关键词:Intrusion detection; data communication; Genetic Algorithm; Tabu Search; Information System; Electronic Transaction.
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