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Bio-inspiring Cyber Security and Cloud Services: Trends and Innovations pp 225–247Cite as

Multilayer Machine Learning-Based Intrusion Detection System

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Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 70))

Abstract

Almost daily we hear news about a security breach somewhere, as hackers are constantly finding new ways to get around even the most complex firewalls and security systems. This turned the security into one of the top research areas. Artificial Immune Systems are techniques inspired by biological immune system—specifically the human immune system—which basic function is to protect the body (system) and defend against attacks of different types. For this reason, many have applied the artificial immune system in the field of network security and intrusion detection. In this chapter, a basic model of a multi-layer system is discussed, along with the basics of artificial immune systems and network intrusion detection. An actual experiment is included, which involved a layer for data preprocessing and feature selection (using Principal Component Analysis), a layer for detectors generation and anomaly detection (Using Genetic Algorithm with Negative Selection Approach), and finally a layer for detected anomalies classification (using decision tree classifiers). The principle interest of this work is to benchmark the performance of the proposed multi-layer IDS system by using NSL-KDD benchmark data set used by IDS researchers. The obtained results of the anomaly detection layer shows that up to 81 % of the attacks were successfully detected as attacks. The results of the classification layer demonstrated that naive bayes classifier has better classification accuracy in the case of lower presented attacks such as U2R and R2L, while the J48 decision tree classifier gives high accuracy up to 82 % for DoS attacks and 65.4 % for probe attacks in the anomaly traffic.

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Author information

Authors and Affiliations

  1. Scientific Research Group in Egypt (SRGE), Cairo University, Cairo, Egypt

    Amira Sayed A. Aziz & Aboul Ella Hassanien

  2. Faculty of Business Administration and Information Systems, Université Française d’Egypte (UFE), Cairo, Egypt

    Aboul Ella Hassanien

Authors
  1. Amira Sayed A. Aziz
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  2. Aboul Ella Hassanien
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Correspondence to Amira Sayed A. Aziz .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Cairo, Egypt

    Aboul Ella Hassanien

  2. Multimedia Control and Assurance Laboratory, Hannam University, Taejeon, Korea, Republic of (South Korea)

    Tai-Hoon Kim

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  4. Faculty of Engineering, Al Azhar University, Qena, Egypt

    Ali Ismail Awad

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Aziz, A.S.A., Hassanien, A.E. (2014). Multilayer Machine Learning-Based Intrusion Detection System. In: Hassanien, A., Kim, TH., Kacprzyk, J., Awad, A. (eds) Bio-inspiring Cyber Security and Cloud Services: Trends and Innovations. Intelligent Systems Reference Library, vol 70. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43616-5_9

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  • DOI: https://doi.org/10.1007/978-3-662-43616-5_9

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  • Print ISBN: 978-3-662-43615-8

  • Online ISBN: 978-3-662-43616-5

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