Implementation of Cyber Network’s Attacks Detection System with Deep Learning Designing Algorithms

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.