Saeed Ullah¹, Junsheng Wu^1,*, Mian Muhammad Kamal^2,*, Mohammed K. Alzaylaee³, Heba G. Mohamed^4,5

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084409 - 23 July 2026

Abstract The proliferation of Internet of Things (IoT) devices has introduced unprecedented security challenges, necessitating efficient intrusion detection systems (IDS) capable of operating under severe resource constraints. This research presents a hardware-informed empirical study of quantized neural-network-based intrusion detection for resource-constrained IoT platforms, using an ARM Cortex-M4 deployment target as a reference. We evaluate FP32, FP16, and INT8 TensorFlow Lite model variants derived from a lightweight 1D-CNN and assess their trade-offs in clean-data accuracy, model size, estimated inference latency, estimated energy consumption, and adversarial robustness. INT8-quantized model achieves 99.10% accuracy on clean data while maintaining 97.50%… More >

Abdulaziz A. Alsulami¹, Qasem Abu Al-Haija^2,*, Rayed Alakhtar³, Ahmad J. Tayeb³, Badraddin Alturki³, Huda Alsobhi⁴, Rayan A. Alsemmeari³

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083321 - 23 July 2026

Abstract The rapid growth of the Internet of Things (IoT) devices has increased the attack area of modern networks, which makes effective intrusion detection systems (IDSs) essential to detect attacks that target IoT infrastructures. Federated learning is a promising approach for collaborative model training in the absence of centralized raw data. Conventional federated approaches rely on fixed client participation and static training configurations, which ensure symmetric treatment of clients despite heterogeneous local data distributions. This can limit convergence and degrade detection performance in non-IID conditions. This paper proposes an Adaptive Action-Based Federated Learning (AA-FL) framework for… More >

Adnan Hnaif^1,*, Hanadi Al-Shawabkah², Ayman Alqafaan², Mohammad Alia¹

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082998 - 23 July 2026

Abstract Many fast pattern-matching mechanisms are used in NIDS (Network Intrusion Detection Systems) to filter higher volumes of network traffic prior to invoking expensive rule verification stages. This filtering phase in signature-based engines, such as Snort, needs to preserve exact matching semantics while being able to process at high throughput on commodity hardware. Here, we introduce a hybrid CPU–GPU architecture-aware framework for exact multi-pattern matching based on the Weighted Exact Matching Algorithm (WEMA). WEMA performs the most relevant matching based on deterministic ordered indexing of category units, which eliminates chaotic control flow (which occurs with automata… More >

Mohammed Abdullah Alsuwaiket^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082979 - 23 July 2026

Abstract Conventional deep learning networks impose prohibitive energy requirements on continuously operational network intelligence applications such as anomaly detection, traffic classification, and adaptive Quality-of-Service (QoS) control. This paper proposes NeuroPulse, a spiking-transformer hybrid neural architecture that combines the temporal sparsity of spiking neural networks (SNNs) with the representational power of sparse self-attention, enabling efficient deployment on neuromorphic network processors (NNPs). We propose a Rate-Coded Cross-Attention (RCCA) module, which converts population-coded spike-trains into attention queries, allowing long-range dependency modeling within sub-milliwatt (sub-mW) power budgets. NeuroPulse also supports catastrophe-free continual learning on non-stationary network traffic distributions via a More >

Jackson Diaz-Gorrin^1,*, Candido Caballero-Gil¹, Pino Caballero-Gil¹, Joanna Kolodziej^2,3

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082207 - 23 July 2026

Abstract A lightweight flow-based intrusion detection system is proposed for identifying Mirai-based distributed denial-of-service attacks in Internet of Things (IoT) environments. Efficient intrusion detection at the network edge is essential for resource-constrained IoT deployments, where devices operate with limited processing, memory, and energy resources, making centralized or computationally intensive solutions impractical in real-world scenarios. Network traffic is represented using statistical and temporal features extracted from unidirectional flows constructed from the TII-SSRC-23 dataset. A balanced subset of 10,000 samples is used for training and evaluation, ensuring balanced data distribution and improving generalization across different traffic conditions. Three… More >

Yimeng Liu¹, Xinyu Xu¹, Wangting Xue¹, Shigen Shen^1,*, Xiao-Zhi Gao²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081775 - 23 July 2026

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 More >

Pardis Sadatian Moghaddam¹, Mahyar Mahmoudi², Nuria Serrano³, Francisco Hernando-Gallego⁴, Diego Martín^3,*, José Vicente Álvarez-Bravo³

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081311 - 23 July 2026

Abstract The rapid proliferation of the Internet of Things (IoT) and cyber-physical systems (CPS) within critical infrastructure sectors has significantly expanded the attack surface for advanced and stealthy cyber threats. Since these systems increasingly rely on real-time data exchange and autonomous control, developing intelligent, scalable, and adaptive anomaly detection mechanisms has become a pressing requirement. This paper proposes a novel hybrid framework, evolutionary-transformer-long short-term memory (Evo-Transformer-LSTM), that integrates the temporal modeling capability of LSTM networks, the global attention mechanism of Transformer encoders, and the optimization power of the improved chimp optimization algorithm (IChOA) for hyper-parameter tuning.… More >

Ulysses Lam^1,*, Jin-Hee Cho², Hyuk Lim³, Terrence Moore⁴, Frederica Free-Nelson⁴, Hyunjae Kang¹, Dan Dongseong Kim¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.084062 - 30 June 2026

Abstract Autonomous vehicles are potentially more vulnerable to cyber-attacks compared to traditional human-driven ones, as they employ electronic sensors to enable self-driving. Cybersecurity for autonomous vehicles will be crucial in the near future. However, intrusion detection systems (IDSes) for vehicles are still in the early stages. Many IDS models that claim to work for vehicles are actually built with traditional Internet datasets rather than those with real vehicle data, which is impractical in reality. In this paper, IDS models are developed with Federated Learning (FL) with the Car-Hacking and CAN-MIRGU datasets, which are obtained from real More >

Arvind Prasad^1,*, Ibrahim Aljubayri², Mohammad Zubair Khan^3,*, Abdulfattah Noorwali⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082021 - 30 June 2026

Abstract Intrusion detection in large-scale IoT deployments becomes particularly challenging during ongoing attack scenarios, where malicious traffic may temporarily dominate benign traffic. In such conditions, streaming network data exhibits severe class imbalance in favor of attack traffic, while device behavior remains heterogeneous, non-identically distributed (non-IID), and temporally evolving. Within federated learning environments, this imbalance can destabilize early aggregation rounds, dominant attack gradients bias the global model, distort decision boundaries, and degrade reliable discrimination of residual benign behavior. Since the server has no access to raw data, these effects can persist across communication rounds if not addressed… More >

Qiuguo Guan, Zhiyu Ren^*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082704 - 15 June 2026

Abstract To address trust-score drift and unsafe online adaptation under cross-domain attack-contaminated streams in Industrial Internet of Things (IIoT) edge environments, this paper proposes a risk-aware lightweight test-time adaptation (TTA) framework, named RaL-TTA, for dynamic trust evaluation of edge nodes. RaL-TTA constructs a low-dimensional robust feature space and a source-domain normal-entropy reference baseline, and performs selective online maintenance in the target domain through Kolmogorov–Smirnov (KS) drift detection, SafeBrake risk gating, Adaptive Batch Normalization (AdaBN) anchor protection, and budgeted sample-level safeguards. Low-risk batches are adapted by updating only lightweight Batch Normalization (BN) parameters, whereas high-risk batches freeze… More >