2026· Computers, Materials & Continua· Vol 88, pp. 1-10· 0 citations· 28 references
TL;DR
This work proposes a deep spiking Q-network-based intrusion detection system (DSQN-IDS) for the IIoT, formulating unknown intrusion detection as a Markov decision process (MDP), and employs a hierarchical multi-stage decision-making framework.
Abstract
: The rapid growth of the Industrial Internet of Things (IIoT) has become a cornerstone of high-quality global economic development. By integrating sensor networks, edge computing, and cloud intelligence, IIoT has emerged as a key enabler for smart manufacturing and digital transformation across industries. However, this technological advancement introduces significant cybersecurity challenges that render traditional intrusion detection systems inadequate for IIoT environments. To address this critical gap, we propose a deep spiking Q-network (DSQN)-based intrusion detection system (DSQN-IDS) for the IIoT, formulating unknown intrusion detection as a Markov decision process (MDP). The system employs a hierarchical multi-stage decision-making framework integrating conditional variational autoencoders (CVAE) for feature extraction, deep Q-networks (DQN) for reinforcement learning-based decision-making, and spiking neural networks (SNNs) for energy-efficient classification. We train the DSQN using a multi-layer perceptron (MLP) to approximate the state-action value function, and leverage the event-driven nature of SNNs—where neurons only spike when their membrane potential exceeds a threshold—to minimize energy consumption. Extensive experiments on IIoT datasets demonstrate that our approach achieves superior performance in balancing detection accuracy, energy efficiency, and model stability when identifying unknown attacks compared to state-of-the-art methods.
Rapid growth in the Internet of Things (IoT) networks means that almost limitless connectivity of intelligent devices and advanced technologies in the domains of healthcare, transport, industrial processes, and smart city automation are possible. However, the mass rollout of IoT devices with limited resources has exposed IoT networks to numerous cyber threat vectors including botnets, distributed denial-of-service (DDoS) attacks, the spread of malware, and unauthorized intrusions to computer networks. This has prompted the evolution of Intrusion Detection Systems (IDSs) from traditional machine learning (ML) techniques to various advanced deep learning techniques used for the detection and analysis of sophisticated cyberattack techniques on network traffic. Within this context, deep neural networks, convolutional neural networks, recurrent neural networks, and various autoencoder models, have all produced significant results with respect to attack detection; however, practical implementations have been constrained by a considerable amount of time and space resources. This has led to a focused interest in quantum-inspired learning approaches, which improve feature distinguishability for normal and attack traffic through the application of high dimensional probabilistic mappings. In addition, frameworks that utilize parameter-efficient design based on tensor decomposition, low-rank, and Kronecker factorizations, have reduced the time and space resources needed for attack detection. A comprehensive overview of IoT intrusion detection research, including benchmark datasets and ML and deep learning models, as well as quantum-inspired and structured neural approaches is provided in this paper. Additionally, current approaches are evaluated on their strengths and weaknesses, and major research gaps are noted. Lastly, the focus is on the development of the next generation of IoT security that is intelligent, scalable, and able to provide real-time protection.
K. Nandana, R. Akarapu· 2026 6th International Confe...· 0 citations
The rapid development of 5G-based Industrial IoT (IIoT) networks has increased their vulnerability to cyberattacks, especially intrusion attempts and botnets. To address the need for an efficient and accurate real-time intrusion detection system, this paper presents an intelligent hybrid deep learning framework based on a combination of deep neural networks (DNNs) and random forests (RF). For the purpose of analyzing high-dimensional characteristics of IIoT data, the suggested system makes use of sophisticated data preprocessing, feature selection via the use of SelectKBest, class imbalance correction through the use of SMOTE, and deep learning methods for hidden-layer expression. The model was evaluated on a state-of-the-art and realistic IIoT cybersecurity dataset, the Edge-IIoTset, which includes 61 highly correlated features spanning 14 attack types. Experimental results show that the proposed hybrid model is more effective than traditional machine learning techniques (DT, RF, SVM, KNN) and a single DNN model. This model meets the real-time requirements of IIoT applications with 99.20% accuracy, no false alarms, and extremely low inference latency of 0.03 to 0.05 milliseconds per sample. This approach makes 5G IIoT networks more secure, faster, and more adaptable. The results help ensure the security of 5G IIoT networks for use in critical areas such as smart factories, cyber-physical systems, power grids, and industrial automation.
Rohan Rajoriya, Shweta Chouksey· International journal of com...· 0 citations
The advent of Internet of Things (IoT) and Cyber-Physical Systems (CPS) has led to the rapid development of highly dynamic and complex communication infrastructures in various domains ranging from healthcare, transportation, industrial automation to intelligent energy systems. Even though intrusion detection and network security is a wellresearched research area, existing intrusion detection systems cannot efficiently overcome shortcomings such as unknown threat detection, false-positive alert detection, network adaptivity and improved accuracy with large-scale real-time heterogeneous traffic data. In order to overcome these challenges, this paper proposes an AI-enabled threat detection framework using hybrid deep learning techniques for intelligent cyber threat analysis and intrusion detection. First, network traffic data is pre-processed, Min-Max-normalized, and enhanced by feature selection along with the Principal Component Analysis (PCA)-based dimension reduction to minimise the redundancy and to improve the quality of the dataset. Second, the optimized feature set is leveraged for AIenabled detection of anomalies using Autoencoder, spatial traffic patterns detection using Convolutional Neural Network (CNN), and the temporal dependencies of virtual attacks in traffic data using an LSTM-Recurrent Neural Network (LSTM-RNN). Finally, a hybrid Deep Neural Network (DNN) and Decision Tree classifier on the output of the hybrid model, classifies the normal and malicious network traffic with a reduced false-positive rate. Experimental results of the proposed framework on network intrusion datasets confirmed the efficiency of the proposed framework significantly outperforming the existing standalone deep learning approaches in terms of accuracy, precision, recall, F1-score, scalability, and real-time cyber threat detection.
Ponugoti Kalpana, Pati Ankitha· 2026 5th International Confe...· 0 citations
A two-tier hybrid IDS that uses a Random Forest model for quick initial detection and a Neural Network for deeper analysis of suspicious traffic is proposed that provides a balanced and efficient solution that overcomes key limitations of existing IDS models and offers a pathway towards a more robust real-time IoT intrusion detection.
R. Paper, Wong Zoey, Y. Watanabe et al.· International Journal of Eme...· 0 citations
The interconnected systems, cloud infrastructures, Internet of Things (IoT) devices, and large networks of today have increased the complexity and frequency of cyberattacks. There is now a growing requirement for intelligent and adaptive Intrusion Detection Systems (IDS). Classic signature-based and machine learning-based IDS methodologies usually fail to recognise advanced, evolving, or zero-day attacks due to their single-feature extraction capabilities and reliance on handcrafted rules. Accordingly, Deep Learning (DL) methods have surfaced with the ability of learning features automatically for better detection performance in other networks. This survey presents recent developments that have taken place in deep learning-based intrusion detection systems with supporting statistics from thirty-six representative works published across IoT, cloud, enterprise and SDN. This paper systematically develops a taxonomy of IDS architectures and a survey of recently proposed networks, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs, GRUs, Autoencoders, GANs, Transformer-based, as well as hybrids. The performance of models has been evaluated using popular benchmark datasets such as NSL-KDD, UNSW-NB15, CICIDS2017, CICIDS2018, N-BaIoT and CICIoT2023. Many studies reported a detection accuracy more than 98%. Innovative methods such as feature selection, data augmentation, hyperparameter optimisation and spatial temporal learning have been discussed in detail. Although strides have been made yet numerous challenges still loom large, from class imbalance to high computational costs and limited interpretability of models. In the conclusion section, we highlight some of the important research challenges and future directions. Further, we discuss explainable artificial intelligence, adversarial robustness, federated learning, lightweight edge-deployable models, and Transformer-based IDS frameworks. According to this survey, this review will help researchers and practitioners in understanding the current developments and in designing the next generations intelligent intrusion detection systems.
Sajith K. V., Gripsy Paul, Bhagavant Deshpande et al.· Journal of Network and Infor...· 0 citations
Mobile Ad hoc Networks (MANET) are broadly used in crucial application in globally; nevertheless, their dynamic topology, open communication environment and limited resource make highly vulnerable to cyber-attacks, necessitates intelligent and strong security solution. Therefore, in this work, implemented an Optimized Spiking Neural Network (SNN) based Intrusion Detection System (IDS) for improving security of MANET environments. UNSW-NB15 dataset is evaluated, with data preprocessing involves data cleaning and handling missing values for enhancing data quality and feature engineering which includes Exploratory Data Analysis (EDA) and normalization for identifying attack pattern and stabilizing learning feature scale. SNN is designed for modelling the temporal characteristics of network traffic, while the White Shark Optimization (WSO) is employed for automatically tuning the network parameters for enhancing detection accuracy. Using Python software, the proposed work attains an accuracy of 94.71%, precision and specificity of 100%, recall of 94.15%, and F1-score of 96.99%, significantly outperforming conventional detection approaches.
R. Ramya, S. Rosaline, K. Kavin et al.· International Conference on...· 0 citations