AUTHOR=Jin Lizhong , Fan Rulong , Han Xiaoling , Cui Xueying 

TITLE=IGSA-SAC: a novel approach for intrusion detection using improved gravitational search algorithm and soft actor-critic

JOURNAL=Frontiers in Computer Science

VOLUME=Volume 7 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2025.1574211

DOI=10.3389/fcomp.2025.1574211

ISSN=2624-9898

ABSTRACT=BackgroundNetwork intrusion detection is a critical component of maintaining network security, especially as cyber threats become increasingly sophisticated. While deep learning-based intrusion detection algorithms have shown promise, they often struggle with high-dimensional datasets containing outliers, anomalies, or rare events. This study addresses these challenges by proposing a novel approach that combines the Improved Gravitational Search Algorithm (IGSA) with the Soft Actor-Critic (SAC) reinforcement learning algorithm, aiming to enhance detection accuracy and computational efficiency.MethodsWe introduce the IGSA-SAC intrusion detection model, which leverages an enhanced Gravitational Search Algorithm (IGSA) to improve robustness against outliers and dynamically adjust the exploration-exploitation balance. This is achieved through fitness normalization with an Adaptive Search Radius and a sigmoid function to modulate the gravitational constant. The IGSA-SAC method effectively navigates the search space to identify the most relevant features for intrusion detection, reducing dimensionality and computational complexity. Additionally, we design a reinforcement learning reward function to guide the learning process, encouraging the agent to improve detection effectiveness while minimizing false alarms and missed detections.ResultsExperiments were conducted on the NSL-KDD and AWID datasets to evaluate the performance of IGSA-SAC. The results demonstrate that IGSA-SAC achieves an accuracy of 84.15% and an F1-score of 84.85% on the NSL-KDD dataset. On the AWID dataset, IGSA-SAC surpasses 98.9% in both accuracy and F1-score, outperforming existing intrusion detection algorithms.ConclusionsThe proposed IGSA-SAC method significantly improves intrusion detection performance by effectively handling high-dimensional datasets and reducing computational complexity. The results highlight the potential of IGSA-SAC as a robust and efficient solution for real-world network intrusion detection systems, offering enhanced accuracy and reliability in identifying cyber threats.