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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Improving Intrusion Detection System using Artificial Neural Network

Author 1: Marwan Ali Albahar
Author 2: Muhammad Binsawad
Author 3: Jameel Almalki
Author 4: Sherif El-etriby
Author 5: Sami Karali

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0110670

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 6, 2020.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Currently, network communication is more suscep-tible to different forms of attacks due to its expanded usage, accessibility, and complexity in most areas, consequently imposing greater security risks. One method to halt attacks is to identify different forms of irregularities in the data transmitted and processed during communication. Detection of anomalies is a vital process to secure a system. To this end, machine learning plays a key role in identifying abnormalities and intrusion in communica-tion over a network. The term regularization is one of the major aspects of training machine learning models, in which, it plays a primary role in several successful Artificial neural network models, by inducing regularization in the model training. Then, this technique is integrated with an Artificial Neural Network (ANN) for classifying and detecting irregularities in network communication efficiency. The purpose of regularization is to discourage learning a more flexible or complex model. Thus, the machine learning model generalizes enough to perform accurately on unseen data. For training and testing purposes, NSL-KDD, CIDDS-001 (External and Internal Server Data), and UNSW-NB15 datasets were utilized. Through extensive experiments, the proposed regularizer reaches higher True Positive Rate (TPR) and precision compared L1 and L2 norm regularization algorithms. Thus, it is concluded that the proposed regularizer demonstrates a strong intrusion detection ability.

Keywords: New regularizer; anomaly detection; NSL-KDD dataset; CIDDS-001 dataset; UNSW-NB15

Marwan Ali Albahar, Muhammad Binsawad, Jameel Almalki, Sherif El-etriby and Sami Karali, “Improving Intrusion Detection System using Artificial Neural Network” International Journal of Advanced Computer Science and Applications(IJACSA), 11(6), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110670

@article{Albahar2020,
title = {Improving Intrusion Detection System using Artificial Neural Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110670},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110670},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {6},
author = {Marwan Ali Albahar and Muhammad Binsawad and Jameel Almalki and Sherif El-etriby and Sami Karali}
}


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