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A Novel Algorithm for Network Anomaly Detection Using Adaptive Machine Learning

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Progress in Advanced Computing and Intelligent Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 564))

Abstract

Threats on the Internet are posting high risk to information security and network anomaly detection has become an important issue/area in information security. Data mining algorithms are used to find patterns and characteristic rules in huge data and this is very much used in Network Anomaly Detection System (NADS). Network traffic has several attributes of qualitative and quantitative nature, which needs to be treated/normalized differently. In general, a model is built with the existing data and the system is trained with the model and then used to detect intrusions. The major and important issue with such NADS is that the network traffic changes over time; in such cases, the system should get trained automatically or retrained. This paper presents an adaptive algorithm that gets trained according to the network traffic. The presented algorithm is tested with Kyoto University’s 2006+ Benchmark dataset. It can be observed that the results of the proposed algorithm outperform all the known/commonly used classifiers and are very much suitable for network anomaly detection.

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Authors and Affiliations

  1. Department of Computer Science, Govt. Arts College, Tiruchirappalli, Tamilnadu, India

    D. Ashok Kumar

  2. Aeronautical Development Agency (Ministry of Defence, GoI), Bengaluru, 560017, India

    S. R. Venugopalan

Authors
  1. D. Ashok Kumar
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  2. S. R. Venugopalan
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Corresponding author

Correspondence to D. Ashok Kumar .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science, Bialystok University of Technology, Białystok, Poland

    Khalid Saeed

  2. Dept. of Computer Science & Engg., University of Calcutta Dept. of Computer Science & Engg., Kolkata, West Bengal, India

    Nabendu Chaki

  3. C. V. Raman College of Engineering, Bhubaneswar, Odisha, India

    Bibudhendu Pati

  4. Department of Computer Science and Engineering, National Institute of Technology, Rourkela, Rourkela, Odisha, India

    Sambit Bakshi

  5. Department of Computer Science and Engineering, National Institute of Technology, Rourkela, Rourkela, Odisha, India

    Durga Prasad Mohapatra

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Ashok Kumar, D., Venugopalan, S.R. (2018). A Novel Algorithm for Network Anomaly Detection Using Adaptive Machine Learning. In: Saeed, K., Chaki, N., Pati, B., Bakshi, S., Mohapatra, D. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 564. Springer, Singapore. https://doi.org/10.1007/978-981-10-6875-1_7

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  • DOI: https://doi.org/10.1007/978-981-10-6875-1_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6874-4

  • Online ISBN: 978-981-10-6875-1

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