Authors - Binh Pham Nguyen Thanh, Long Duong Phi, Phung Thi-Kim Nguyen, Nhan Thi Cao Abstract - The rapid proliferation of Internet of Things (IoT) devices has significantly increased the digital attack surface, which, in turn, has raised network vulnerability to sophisticated Distributed Denial of Service (DDoS) campaigns that could reduce the effectiveness of traditional signature-based Intrusion Detection System (IDS). Furthermore, conventional Machine Learning (ML) approaches are often subject to manual feature engineering and lack the capture of complex spatial and temporal dependencies, which are essential to detect subtle, polymorphic threats. In this regard, the present work proposes a lightweight hybrid Deep Learning (DL) architecture for reliable (DDoS) detection. The proposed approach integrates spatial feature extraction using a Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) network to capture temporal correlations, further enhanced by an additive attention mechanism that underlines the importance of flow segments relevant to recognition. To mitigate issues with computational complexity, a two-phase hybrid feature selection approach, a combination of Information Gain (IG) and Dynamic Particle Swarm Optimization (PSO) would be utilized to select an optimal subset of features. The performance of the model was evaluated using the CICDDoS2019 benchmark dataset. The feature selection process was able to reduce the input space from 80 to 17 relevant features. The combined CNN-BiLSTM model, along with threshold optimization, was able to achieve an accuracy of 94.1%, which indicates a significant improvement in the reduction of false negatives and validates the effectiveness of the proposed method in a secure IoT environment.
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