A COMPREHENSIVE REVIEW OF DUAL FEATURE-BASED INTRUSION DETECTION SYSTEM FOR IoT NETWORK SECURITY

Computer Science and Engineering

The rapid proliferation of Internet of Things (IoT) devices has revolutionized connectivity across sectors like healthcare, smart cities, and industrial automation, yet it has amplified vulnerabilities to cyber threats such as distributed denial-of-service (DDoS) attacks, malware infiltration, and unauthorized access. This survey paper provides a comprehensive overview of Intrusion Detection Systems (IDS) tailored for IoT environments, emphasizing the evolution from traditional signature-based methods to advanced machine learning (ML) and deep learning (DL) approaches. We analyze key challenges, including resource constraints of IoT devices, heterogeneous network traffic, and the need for real-time detection with minimal false alarms. Drawing from recent literature, we examine hybrid models that integrate optimization algorithms with neural networks to enhance feature selection and classification accuracy. A focal point is the Dual Feature Optimized Using Deep Learning Network (FOUND) technique, which employs Bald Eagle Search (BES) and Butterfly Optimization Algorithm (BOA) for dual-path feature extraction (flow-level and packet-level), followed by Multi-Head Attention-based Bidirectional Gated Recurrent Unit (MHA-BiGRU) for precise attack classification. Evaluations on datasets like BoT-IoT and UNSW-NB15 reveal FOUND's superior performance, achieving up to 99.02% accuracy and low false alarm rates compared to benchmarks like Blockchain-based African Buffalo with Recurrent Neural Network (BbAB-RNN) and Golden Jackal Optimization with Deep Learning (GJOADL-IDSNS). This review synthesizes over 20 studies, highlighting trends in DL-based IDS, such as Long Short-Term Memory (LSTM) variants and graph neural networks, while identifying gaps like handling imbalanced data and scalability in edge computing. Future directions include federated learning for privacy-preserving IDS and integration with blockchain for tamper-proof detection. Overall, this survey underscores the critical role of adaptive, efficient IDS in securing IoT ecosystems against evolving threats, offering insights for researchers and practitioners to develop robust solutions

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