Advancing Intrusion Detection Systems: A Comprehensive Review of Deep Learning and Hyperparameter Optimization Techniques

Abstract

In the suddenly changing realm of cyber security, Intrusion Detection Systems (IDS) are essential for protecting computer networks from harmful actions.  This survey paper offers an extensive examination of sophisticated approaches and procedures utilised in IDS, emphasising the amalgamation of deep learning (DL) and hyper parameter optimisation.  We examine many categories of cyber-attacks that confront conventional IDS, including phishing, malware, ransom ware, and advanced evasion strategies.  The study explores the intricacies of hyper parameter tuning in DL algorithms, emphasising major methods such grid search (GS), random search (RS), Bayesian optimisation (BO), and genetic algorithms (GA).  This paper examines recent progress in DL applications for IDS, highlighting the efficacy of models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and auto encoders in identifying both established and emerging threats.  Case studies and contemporary research illustrate the effects of these tactics on enhancing IDS accuracy, minimising false positives, and responding to novel attack strategies.  This survey highlights the necessity for on-going advancement in IDS to tackle the evolving nature of cyber threats and improve the overall security stance of networked systems.