Original scientific paper
https://doi.org/10.32985/ijeces.15.10.1
Implementation of Cyber Network’s Attacks Detection System with Deep Learning Designing Algorithms
Lubna Emad Kadhim
; University of Imam Al-Kadhum, College of Imam Al-Kadhum (IKC), Department of Computer Techniques Engineering 10011, Baghdad, Iraq
Saif Aamer Fadhil
; University of Imam Al-Kadhum, College of Imam Al-Kadhum (IKC), Department of Computer Techniques Engineering 10011, Baghdad, Iraq
Sumaia M. Al-Ghuribi
; Prince Sattam bin Abdulaziz University, Faculty of Computer Engineering & Sciences, Department of Software Engineering Alkharj 11942, Riyadh, Saudi Arabia. Taiz University, Faculty of Applied Sciences, Department of Computer Science, Taiz, Yemen
Amjed Abbas Ahmed
; Universiti Kebangsaan Malaysia (UKM), Faculty of Information Science and Technology, Center for Cyber Security, 43600, Bangi, Malaysia
*
Mohammad K. Hasan
; Universiti Kebangsaan Malaysia (UKM), Faculty of Information Science and Technology, Center for Cyber Security, 43600, Bangi, Malaysia
Shahrul A. Mohd Noah
; Universiti Kebangsaan Malaysia (UKM), Faculty of Information Science and Technology, Centre for Artificial Intelligence Technology (CAIT) 43600, Bangi, Malaysia
Fatima N. AL-Aswadi
; UCSI University, Institute of Computer Science and Digital Innovation 56000, Kuala Lumpur, Malaysia Hodeidah University, Faculty of Computer Science and Engineering Al Hudaydah, Yemen
* Corresponding author.
Abstract
The internet has become indispensable for modern communication, playing a vital role in the development of smart cities and communities. However, its effectiveness is contingent upon its security and resilience against interruptions. Intrusions, defined as unauthorized activities that compromise system integrity, pose a significant threat. These intrusions can be broadly categorized into host intrusions, which involve unauthorized access and manipulation of data within a system, and network intrusions, which target vulnerabilities within the network infrastructure. To mitigate these threats, system administrators rely on Network Intrusion Detection Systems (NIDS) to identify and respond to security breaches. However, designing an effective and adaptable NIDS capable of handling novel and evolving attack strategies presents a significant challenge. This paper proposes a deep learning-based approach for NIDS development, leveraging Self-Taught Learning (STL) and the NSL-KDD benchmark dataset for network intrusion detection. The proposed approach is evaluated using established metrics, including accuracy, F-measure, recall, and precision. Experimental results demonstrate the effectiveness of STL in the 5-class categorization, achieving an accuracy of 79.10% and an F-measure of 75.76%. This performance surpasses that of Softmax Regression (SMR), which attained 75.23% accuracy and a 72.14% F-measure. The paper concludes by comparing the proposed approach's performance with existing state-of-the-art methods.
Keywords
cyber network; deep learning; intrusion detection system; network intrusion;
Hrčak ID:
322475
URI
Publication date:
19.11.2024.
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