Bio-Inspired Feature Selection and Deep Learning for DDoS Detection: A Systematic Review

Distributed Denial of Service (DDoS) attacks represent one of the most persistent and damaging cyber threats facing cloud computing environments, with global attack volumes increasing from 7.9 million incidents in 2018 to over 15 million in 2023. The high dimensionality of modern network traffic datasets which often containing hundreds of statistical features, poses a significant challenge for building efficient and accurate intrusion detection systems (IDS). This paper presents a systematic review of bio-inspired metaheuristic algorithms for feature selection in DDoS detection, coupled with deep learning classification architectures. A structured literature search was conducted across IEEE Xplore, Scopus, Web of Science, and Google Scholar using defined inclusion and exclusion criteria, yielding a final corpus of studies published between 2015 and 2025. Unlike existing reviews that treat feature selection and deep learning classification in isolation, this review uniquely integrates both dimensions within a unified analytical framework, with particular emphasis on cloud-specific DDoS scenarios, multi-stage hybrid feature selection pipelines, and cascaded deep learning architectures operating on learned latent representations. The review examines the landscape of DDoS attack taxonomies, evaluates eight widely used benchmark datasets, and provides a comparative analysis of seven bio-inspired feature selection algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Ant Colony Optimization (ACO), Whale Optimization Algorithm (WOA), Firefly Algorithm (FA), and Salp Swarm Algorithm (SSA). Deep learning architectures surveyed include Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and hybrid cascaded models. A critical analysis of reported performance metrics is provided, addressing methodological concerns including dataset imbalance, overfitting risks, and limited cross-dataset generalisation. Seven key research gaps are identified, and future research directions encompassing multi-stage hybrid feature selection pipelines, multi-class detection taxonomies, and explainable AI integration are proposed.

DDoS Detection, Intrusion Detection System, Bio-Inspired Feature Selection, Deep Learning, Cloud Security

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