Aug 2026· International journal of computer information systems and industrial management applications· 0 citations
TL;DR
This research proposes a novel framework for anomaly detection in WSNs that leverages federated deep learning and prioritizes real-time adaptation and data privacy, and offers a promising path forward for securing WSNs by enabling distributed, privacy-preserving anomaly detection with real-time adaptation capabilities.
Abstract
Wireless Sensor Networks (WSNs) play a crucial role in various applications, but their vulnerability to malicious nodes and data breaches hinders their full potential. Traditional security methods often struggle to keep pace with evolving attack patterns and can introduce privacy concerns. This research proposes a novel framework for anomaly detection in WSNs that leverages federated deep learning and prioritizes real-time adaptation and data privacy. Sensor nodes collaboratively train adaptive deep learning models to identify anomalies in real-time, enabling continuous learning and response to evolving threats. Partial Homomorphic Encryption (PHE) safeguards sensitive data throughout the network, ensuring data confidentiality. The trade-off between security and computational cost associated with PHE is acknowledged. The effectiveness of the proposed system FedShield-PHE will be evaluated through simulations, comparing its performance to existing methods across various metrics including detection accuracy, network overhead, and energy consumption. This research offers a promising path forward for securing WSNs by enabling distributed, privacy-preserving anomaly detection with real-time adaptation capabilities.
A Distributed Denial of Service (DDoS) attack can be launched using the vast number of edge connected devices in Software Defined AIoT systems. The shortage of modern labeled training data makes centralized defenses ineffective, while privacy concerns restrict sharing sensitive traffic information. To address these challenges, this work proposes a decentralized defense framework for AIoT based on a trustworthy Federated Learning (FL) architecture. The framework integrates three key components. First, a secure multiparty computation based aggregation scheme enables privacy preserving exchange of local model updates while remaining robust to outliers. Second, a horizontal cooperation based mitigation mechanism reduces packet loss and limits attack impact on legitimate traffic. Third, a semi supervised FL based anomaly detection strategy leverages a small number of labeled outliers to accurately identify attack classes while reducing false alarms. Experiments conducted on real world datasets, including CICIDS2017 and InSDN, show that the proposed approach significantly outperforms state of the art SDN based protection systems in accuracy and efficiency. The study further examines FL enabled by Software Defined Networking as a paradigm that improves security and reduces training time without degrading learning performance. However, SDN assisted FL introduces new vulnerabilities, particularly DDoS attacks targeting the SDN control plane and disrupting model parameter exchanges. To mitigate this risk, a contrastive learning based detector using a two layer deep neural architecture is introduced. The LSTM SVM achieves 99.75 percent accuracy and 99.80 percent recall, while the CNN BiGRU reaches 99.36 percent accuracy and 99.55 percent recall in practical deployments scenarios.
P. Parthasarathi, Sasikala Dhamodaran, K. Shree et al.· International journal of sof...· 0 citations
Federated Learning enables distributed intrusion detection in IoT networks but suffers from gradient leakage vulnerabilities that can expose sensitive client data through reconstruction attacks. This paper presents an adaptive privacy-preserving framework that dynamically adjusts Differential Privacy parameters using a meta-learning-based controller, optimising the privacy utility trade-off throughout FL training. We have previously proposed a deep reinforcement learning-based intrusion detection framework that achieved high detection accuracy in IoT environments, but its gradient exchanges remained vulnerable to potential data leakage, motivating the need for a stronger privacy-preserving mechanism. Unlike conventional static-noise DP approaches, our method continuously modulates gradient clipping and Gaussian perturbation levels in response to learning dynamics. We evaluate the framework on three benchmark datasets: ToN-IoT, CICIOT2023, and CICIDS2017, demonstrating that adaptive DP significantly reduces gradient reconstruction vulnerability compared to unprotected FL while maintaining detection accuracies of 99.68%, 98.33%, and 97.25%, and Macro-F1 scores of 98.04%, 84.67%, and 86.08% on ToN-IoT, CICIoT2023, and CICIDS2017, respectively, under a cumulative privacy budget of 4.76. Our approach outperforms fixed-noise baselines by preserving higher detection performance at equivalent privacy levels. These findings establish adaptive privacy modulation as a practical and secure solution for federated intrusion detection in real-world IoT deployments.
Elham Aldhamari, Shaleeza Sohail, N. Udzir et al.· International Conference on...· 0 citations
Deep learning is becoming popular in cloud applications and serves to provide intelligent services; data aggregation in a central location makes sensitive information vulnerable to privacy breaches, regulatory infractions, and adversarial manipulation. All modern privacy mechanisms offer partial protection and frequently lack accuracy, scalability, or practicality in their operations. To overcome these limitations, a federated deep learning model is formulated so that secure joint learning can occur without transferring raw data across the domains of ownership. The framework incorporates training that is decentralized, training that uses differential privacy, training that uses secure aggregation, training that uses encrypted communication, and training that uses trust-based anomaly defense to defend against leakage, poisoning, and inference attacks. It also supports heterogeneous and highly non-IID datasets using adaptive coordination and stability-relevant participation regulation and meets emerging data protection requirements. The methods of resource-conscious orchestration and the optimization of communication eliminate overhead without obstructing the effectiveness of learning. The paradigm has therefore formed a privacy-by-design intelligent cloud ecosystem which ensures confidentiality, maintains performance, enhances robustness, and ensures responsible AI implementation in privacy-related sectors of healthcare, finance, governance, and smart infrastructure.
Sribidhya Mohanty, Pallavi Gupta, Anil Pratap Singh et al.· 2026 International Conferenc...· 0 citations
The deepening cyber-physical integration of smart grids has expanded the attack surface of power networks, while centralised intrusion detection schemes struggle with data silos, privacy exposure, and prohibitive communication costs across geographically dispersed substations. This paper proposes a federated deep learning framework that addresses these constraints jointly. A three-tier cloud-edge-terminal architecture confines raw measurements to local devices and exchanges only model parameters across tiers. At each edge node, a hybrid CNN-BiLSTM detector trained under focal loss captures both spatial protocol motifs and temporal attack signatures, including stealthy false data injection and APT traces. Privacy is preserved through a layer-selective mechanism that combines Paillier homomorphic encryption on sensitive gradient slices with calibrated differential privacy on the residual components, pushing the privacy-utility frontier outward without saturating cryptographic cost. An adaptive aggregation rule weights client updates by data quality, drift severity, and marginal validation contribution, mitigating the convergence pathologies that vanilla FedAvg exhibits under sharp non-IID partitioning. Experiments on NSL-KDD, CICIDS2017, and an ICS power-system corpus show that the proposed scheme recovers within 0.7 F1 points of the centralised upper bound, suppresses membership inference advantage to below 0.08, holds detection quality against up to 20% Byzantine clients, and converges in roughly half the rounds required by FedAvg. The framework offers a deployable path toward collaborative intrusion detection across regional grid operators without compromising data sovereignty.
Lin Chen, Zhuo Tang, Yiwei Yang et al.· Scientific Reports· 0 citations
The rapid expansion of the Internet of Things (IoT) has intensified cybersecurity risks by exposing distributed connected devices to increasingly complex and pervasive threats. Conventional centralized security mechanisms often struggle to accommodate the heterogeneous and decentralized structure of IoT networks. This study investigates Federated Learning (FL) as a decentralized approach to intrusion detection that enables local model training on IoT edge devices while transmitting only encrypted model updates to a central server, thereby preserving data privacy and reducing communication overhead. A novel FL-based Intrusion Detection System (IDS) architecture was developed using Convolutional Neural Networks (CNNs) for anomaly detection and the Federated Averaging (FedAvg) algorithm for aggregating local model updates. The framework was evaluated on standard IoT datasets under non-independent and identically distributed (non-IID) data conditions to simulate heterogeneous real-world environments. Experimental results demonstrate that the proposed system achieved a detection accuracy of 94.6%, an F1-score of 93.8%, and a recall of 92.7%, outperforming centralized and standalone local learning methods. The framework also reduced communication overhead by 35% and achieved convergence 28% faster than conventional approaches. These findings demonstrate that FL can provide a scalable, privacy-preserving, and computationally efficient foundation for strengthening IoT cybersecurity. This study contributes a decentralized machine-learning architecture for real-time, adaptive, and privacy-conscious intrusion detection in large-scale IoT environments.
Mohammed Ajuji, Y. M. Malgwi, A. Ahmadu et al.· International Journal of Edu...· 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
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