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Machine Learning Techniques for Anomalies Detection and Classification

  • Amira Sayed Abdel-Aziz
  • Aboul Ella Hassanien
  • Ahmad Taher Azar
  • Sanaa El-Ola Hanafi
Part of the Communications in Computer and Information Science book series (CCIS, volume 381)

Abstract

Malicious users are always trying to intrude the information systems, taking advantage of different system vulnerabilities. As the Internet grows, the security limitations are becoming more crucial, facing such threats. Intrusion Detection Systems (IDS) are a common protecting systems that is used to detect malicious activity from inside and outside users of a system. It is very important to increase detection accuracy rate as possible, and get more information about the detected attacks, as one of the drawbacks of an anomaly IDS is the lack of detected attacks information. In this paper, an IDS is built using Genetic Algorithms (GA) and Principal Component Analysis (PCA) for feature selection, then some classification techniques are applied on the detected anomalies to define their classes. The results show that J48 mostly give better results than other classifiers, but for certain attacks Naive Bayes give the best results.

Keywords

Intrusion Detection Intrusion Detection System Network Intrusion Detection Decision Tree Learning Minkowski Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Amira Sayed Abdel-Aziz
    • 1
  • Aboul Ella Hassanien
    • 2
  • Ahmad Taher Azar
    • 3
  • Sanaa El-Ola Hanafi
    • 2
  1. 1.Université Française d’ÉgypteCairoEgypt
  2. 2.Faculty of Computers and InformationCairo UniversityEgypt
  3. 3.Faculty of Computers and InformationBenha UniversityEgypt

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