Jul 2026· Wireless personal communications· Vol 146, pp. 5433 - 5457· 0 citations· 39 references
TL;DR
This work proposes a novel multiclass intrusion detection system using FL at the subnetwork level using various machine learning models, including Artificial Neural Networks, Convolutional Neural Networks, and Long Short-Term Memory to build an effective IoT IDS.
A lightweight, explainable IDS that combines a 1D-CNN for spatial feature analysis with SHAP for model interpretation, yielding streamlined models that preserve over 93% F1-score and reduce computational overhead by more than 38%, facilitating millisecond-level inference on edge hardware.
Miracle Udurume, Vladimir V. Shakhov, Insoo Koo· Scientific Reports· 0 citations
The results show a success in implementing a real time, scalable, privacy-preserving, and adaptive IDS in large-scale IoT deployments through intelligent workload distribution between edge and cloud layers.
Chidera Winifred John, Eduediuyai Ekerete Dan, P. Asuquo et al.· E3S Web of Conferences· 0 citations
The Internet of Things (IoT) has become increasingly integrated into our daily lives, offering a wide range of services through the proliferation of connected devices. While this connectivity enhances convenience and functionality, it also introduces significant security challenges, exposing IoT systems to various forms of cyberattacks. In this paper, we propose a lightweight edge-based intrusion detection approach for massive IoT networks, leveraging a Long Short-Term Memory (LSTM) model to achieve high accuracy with minimal resource consumption. Unlike centralized solutions, the proposed system is fully implemented and deployed at the edge level, enabling local traffic analysis directly on edge devices. This design reduces latency, minimizes bandwidth consumption, enhances data privacy, and ensures real-time detection capabilities in large-scale IoT environments. The approach incorporates an efficient data pre-processing methodology applied to the wellknown Avast IoT-23 dataset, resulting in a detection accuracy of 99.9% with a compact model size of only 1767 KB. To further optimize performance, the system decomposes the data into clusters before applying a tailored LSTM model for each subset. Experimental evaluation using real malicious traffic demonstrates that the proposed model achieves up to 90% specificity and 88% precision under real-world conditions. These results confirm the effectiveness of our edge-level LSTM framework in providing secure, scalable, and resource-efficient intrusion detection for large-scale IoT environments.
Rapid growth in Internet of Things (IoT) devices has expanded the attack surface of modern-day networks. Therefore, it is imperative to deploy an IoT Intrusion Detection System (IDS) for secure communication and reliable operation. However, traditional IDS systems are often inefficient when dealing with high-dimensional data, new attack strategies, and severe imbalanced data problems, leading to low detection performance and high false alarms. In order to solve these problems, this paper introduces a novel intelligent IDS system using the Feature Selection technique inspired by Bowerbird Courtship (BBFS) and Long Short-Term Memory Autoencoder (LSTM-AE) using Seagull Optimizer (SGO). The most important objective here is to build an effective and scalable IDS that can perform efficient feature selection, learn deep temporal dependencies, and tune its hyperparameters to classify malicious and benign traffic more effectively. In this way, we consider all aspects of data preprocessing, feature optimization, balancing, and classification, overcoming the limitations of existing techniques. The evaluation of the CIC IoT 2023 intrusion dataset proves the efficiency of the model, which is shown by high scores: 99.63% of accuracy, 99.55% of detection rate, 99.71% of precision, and 99.59% of F1 score. As seen from the comparison with other models, the BBFS-LSTM-AE-SGO model is better than the compared model in terms of all metrics. It means that the proposed IDS can detect various types of attacks with minimum errors. All through this research has made way for the design of a novel, optimized IoT-IDS model that thereby strengthens cybersecurity resilience, supports real-time monitoring, and hence advances the intrusion detection for IoT-enabled environments.
Nancy Thomas, R. Gunasundari· international journal of eng...· 0 citations
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 growth of the Internet of Things (IoT) in critical domains such as healthcare, smart cities, cybersecurity, and finance has led to the generation of large volumes of distributed data and increased susceptibility to cyberattacks. Although federated learning (FL)-based intrusion detection systems (IDS) have been introduced to support distributed learning and improve privacy, they still face several challenges, including performance limitations, high computation and communication overhead, and potential privacy attacks. To address these challenges, we propose a Random Projection-Based Personalized Federated Learning (RPPFL) framework for IoT intrusion detection. In the proposed framework, random projection, a lightweight one-way dimensionality reduction transformation, is applied at the IoT device level, which enhances privacy while lowering computational and communication costs. Furthermore, personalized federated learning at the fog layer reduces the computational burden on resource-constrained IoT devices and improves model robustness in environments where IoT data are non-independent and identically distributed (non-IID). We also introduce a conditional generative adversarial network (cGAN)-based privacy attack to evaluate the resilience of the proposed framework and demonstrate the effectiveness of our approach in preserving the privacy of IoT data. Experimental results on the RT-IoT 2022 and CIC-IoT 2023 datasets demonstrate that RPPFL provides high detection accuracy (above 95.0%) while preserving data privacy and reducing computation and communication overhead in dynamic IoT environments. The proposed framework is generalizable and applicable to a wide range of IoT intrusion detection scenarios.
Md. Morshedul Islam, Hossain Shahriar, Alfredo Cuzzocrea et al.· Annual International Compute...· 0 citations