The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computer Vision Conference (CVC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2020.0110670
PDF

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

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}
}



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.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Computer Vision Conference
  • Healthcare Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org