Network intrusion detection leveraging machine learning and feature selection

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Ali, Arshid and Shaukat, Shahtaj and Tayyab, Muhammad and Khan, Muazzam A and Khan, Jan Sher and Arshad and Ahmad, Jawad; (2021) Network intrusion detection leveraging machine learning and feature selection. In: 2020 IEEE 17th International Conference on Smart Communities. IEEE, Piscataway, NJ. ISBN 9780738105277 (https://doi.org/10.1109/honet50430.2020.9322813)

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Abstract

Handling superfluous and insignificant features in high-dimension data sets incidents led to a long-term demand for system anomaly detection. Ignoring such elements with spectral instruction not speeds up the analysis process but again facilitates classifiers to make accurate selections during attack perception stage, when wrestling with huge-scale and heterogeneous data. In this paper, for dimensionality reduction of data, we use Correlation-based Feature Selection (CFS) and Naïve Bayes (NB) classifier techniques. The proposed Intrusion Detection System (IDS) classifies attacks using a Multilayer Perceptron (MLP) and Instance-Based Learning algorithm (IBK). The accuracy of the introduced IDS is 99.87% and 99.82% with only 5 and 3 features out of 78 features for IBK. Other metrics such as precision, Recall, F-measure, and Receiver Operating Curve (ROC) also confirm the principal performance of IBK compared to MLP.

ORCID iDs

Ali, Arshid, Shaukat, Shahtaj, Tayyab, Muhammad, Khan, Muazzam A, Khan, Jan Sher, Arshad ORCID logoORCID: https://orcid.org/0000-0001-8621-2773 and Ahmad, Jawad;
  • Item type:Book Section
    ID code:79577
    Dates:
    Date
    Event
    21 January 2021
    Published
    Notes:© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:14 Feb 2022 11:01
    Last modified:12 Dec 2024 01:30
    URI:https://strathprints.strath.ac.uk/id/eprint/79577
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