تطوير نظام لكشف وتصنيف الهجمات في الشبكات الحاسوبية بالاعتماد على خوارزمية الخلايا الجذعية المناعية والخوارزمية الجينية

Abstract

With the growing reliance on its technologies across all aspects of life particularly in light of continuous technological advancements digital networks and their components have become increasingly vulnerable to sophisticated cyber-attacks. As data traffic intensifies and communication methods evolve, the risk of breaches that compromise information integrity and service continuity also rises. This necessitates the development of intelligent and advanced systems capable of detecting threats and responding to them efficiently, matching the pace and complexity of modern attack strategies to ensure system stability and secure data exchange.

This research aims to investigate the role of the immune-inspired Stem Cell Algorithm in enhancing intrusion detection and classification systems within computer networks, due to its ability to process large-scale data and analyze diverse, complex patterns with high accuracy making it a promising tool in cybersecurity for addressing evolving threats.

In this research, we propose an intelligent system that enhances the detection and classification of cyber threats in computer networks by integrating the Stem Cell Algorithm with Genetic Algorithms. The system's performance was evaluated in diverse network environments using the benchmark UNSW-NB15 dataset and implemented via the VSCode environment using Python and its relevant machine learning and data analysis libraries.

The final results of this study demonstrated the effectiveness of the proposed system in accurately classifying attacks and identifying malicious activity in the network, with the ability to intelligently handle uncertain suspicious behavior. The model achieved a high classification accuracy of 97% and a very low false alarm rate of 4%, along with real-time execution capability, highlighting its high efficiency and the significant role of bio-inspired algorithms in enhancing cybersecurity.