Authors : Umaru Mustapha Audu; Zayyanu Umar; Karatu Musa Tanimu; Sirajo Abdullahi Bakura

Volume/Issue : Volume 11 - 2026, Issue 9 - September

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Google Scholar : https://tinyurl.com/454ur6fc

DOI : https://doi.org/10.38124/ijisrt/26sep806

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : The rapid expansion of Internet of Things (IoT) technologies has increased the complexity of network environments and exposed connected devices to diverse cybersecurity threats. Conventional intrusion detection approaches often face limitations in identifying multiple attack categories, particularly in highly imbalanced IoT network traffic. This study proposes a hybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory with Attention (CNN– BiLSTM–Attention) model for multiclass intrusion detection in IoT environments.

Keywords : Internet of Things (IoT); Intrusion Detection System; Deep Learning; Convolutional Neural Network; Bidirectional Long Short-Term Memory; Attention Mechanism; Multiclass Classification; BoT-IoT; SMOTE; Cybersecurity.

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