Enhancing the Security of Internet of Things Networks Through Binarized Simplicial Convolutional Neural Network Based Intrusion Detection Systems for Robust Cyber Threat Mitigation

The Internet of Things (IoT) is rapidly transforming modern smart environments, but its distributed nature and resource-constrained devices pose significant challenges to cybersecurity. To address these problems, this manuscript proposes an intrusion detection method based on Binarized Simplicial Convolutional Neural Networks (IoT-IDS-Bi-SCNN), which integrates advanced preprocessing and feature selection for enhanced IoT security. The raw data from the CIC-IDS2017 dataset are first preprocessed using an Adaptive Fast Desensitized Kalman Filter (AFDKF) to impute missing values and improve data reliability. Later, the most discriminative features are chosen using the Secretary Bird Optimization Algorithm (SBOA) to lower dimensionality and boost learning effectiveness. These features are fed into the Bi-SCNN model, which leverages higher-order simplicial structures and binary operations for efficient and accurate intrusion classification. The proposed model classifies traffic into multiple categories, including Benign, Brute Force, DoS, DDoS, Web Attack, Heartbleed, Infiltration, Botnet and Port Scan. Experimental findings indicate that the proposed IoT-IDS-Bi-SCNN achieves a detection rate of 98.75%, recall of 98.89%, accuracy of 98%, precision of 98.5% and F1-score of 98.77%, while maintaining a low computational time of 1.65 s and outperforms state-of-the-art models like an actual-time intrusion detector for IoT nodes by deep learning (ID-IoT-LSTM), improving IoT network security by a deep learning-powered intrusion detection system (DLP-IDS-FFNN), and a new deep learning-based intrusion detection system for IoT DDoS safety (IDS-IoT-DNN). These findings indicate the proposed method’s suitability for resource-aware IoT intrusion detection.