Enhancing Intrusion Detection Systems: Unleashing the Power of Hybrid Optimization Approaches

Intrusion detection is a vital defense mechanism that protects computer systems, networks, and data from unauthorized access, attacks, and malicious activities. It enables realtime or near real-time detection of security breaches, allowing immediate action to mitigate the impact of intrusions. By monitoring network traffic, system logs, and user activities, Intrusion Detection Systems (IDS) aid in identifying and responding to threats promptly. Moreover, IDS plays a crucial role in analyzing the nature and severity of intrusions, facilitating the implementation of appropriate countermeasures, and enhancing overall security. Compliance with security standards and regulations is also ensured, maintaining customer trust and privacy protection. Traditional IDS approaches often struggle to effectively detect sophisticated and evolving cyber threats. To address these limitations, this article proposes a novel approach to enhance IDS performance through the use of hybrid optimization techniques. The proposed work uses the backpropagation algorithm, a widely used method for training artificial neural networks (ANNs), along with optimization algorithms like Artificial Bee Colony (ABC) and Harmony Search (HS), to optimize intrusion detection performance by selecting the most effective features while reducing resource requirements. The integration of backpropagation neural networks with the ABC and HS algorithms presents a promising solution for enhancing IDS. These hybrid optimization techniques not only enhance detection accuracy but also address the crucial issue of feature selection, reducing IDS complexity and computational requirements. This research contributes to the development of more robust and efficient IDS systems capable of detecting and mitigating advanced cyber threats in real-time network environments