Hybrid Distributed Learning With Knowledge Distillation for Resource-Efficient Intrusion Detection in Distributed Networks

An explainable, resource-efficient transformer-based IDS with adaptive agent routing

FedSE-1DSqueezeNet: a lightweight federated intrusion detection system for Internet of Things

Scenario-Aware Federated Intrusion Detection for V2X-Inspired Edge Security: Calibration, Heterogeneity, and Communication Analysis

The findings support federated learning as a viable and communication-efficient direction for privacy-aware intrusion detection in distributed edge-security settings, while also highlighting the need for cautious interpretation, native V2X validation, and future robustness analysis against compromised federated clients.

Federated Lightweight Intrusion Detection in Drone Swarms with Knowledge Distillation

Drone swarms are increasingly deployed in critical applications such as surveillance, disaster response, and infrastructure monitoring. However, their reliance on open communication channels and their limited computational resources make them vulnerable to a wide range of cyber-threats. There is a growing interest in intrusion detection systems (IDS) specifically designed for drone environments and operations. However, the conventional solutions including Machine Learning (ML)-based approaches require collecting all data from heterogeneous drones in the swarm and processing on a central server may not be always feasible. Federated Learning (FL) has emerged as a promising distributed solution with an additional privacy-preserving feature. Even though potential studies exist, conventional FL-based IDS frameworks still face communication and computational overhead challenges, while achieving a balance between efficiency and effective detection under practical resource constraints remains a challenge. Therefore, we propose a lightweight FL-based IDS tailored for drone swarm networks using deep neural networks (DNN) enhanced with knowledge distillation (KD) to reduce model complexity and communication costs without sacrificing detection performance. We evaluate our framework using Raspberry Pi 4 devices and a real-world drone network dataset. Our approach demonstrates a detection accuracy of approximately 98.6% while reducing overall communication cost by around 70% and computational overhead by 29%. These results show that FL combined with KD is a practical and suitable solution for secure and efficient deployment in resource-constrained drone networks.

Decoding the Enigma of Collaborative Intrusion Detection Systems: Ensemble Learning vs Federated Learning in the Battle for Collaborative Anomaly Detection Supremacy

Coordinated attacks, such as large-scale scanning, worm outbreaks, and Distributed Denial of Service (DDoS) attacks, exhibit distributed cyberattack characteristics that make them challenging to detect with standalone Intrusion Detection Systems (IDS). Collaborative Intrusion Detection Systems (CIDS) address this limitation by aggregating data from multiple network sources and leveraging collective intelligence for anomaly detection, making them more effective in identifying coordinated attacks. CIDS system employs Ensemble Learning (EL) or Federated Learning (FL) to build robust collaborative anomaly detection. EL enhances detection by integrating predictions from multiple models, while FL enables model aggregation from multiple models and preserves privacy. This research examines the comparative effectiveness of EL and FL within CIDS for robust detection of coordinated attacks in heterogeneous network environments. Benchmarking results reveal that network heterogeneity significantly influences detection model performance. Furthermore, this study provides key insights and lessons learned from the comparative analysis, offering a foundation for future research on cyberattack detection using collaborative anomaly detection methods in CIDS.

FET-FIDS: a federated enhanced transformer-based framework for privacy-preserving network intrusion detection.

The rising rate of interconnected systems, cloud infrastructures, edge environments, and distributed network architectures have greatly exposed the vulnerability of the current digital infrastructures. This has exposed them to more advanced cybercrimes like denial-of-service attacks, malware injections, and data leaks. Also, there are sophisticated persistent threats that add more security burdens to such systems. The traditional Intrusion Detection Systems (IDS) are conventionally designed around central data collection and model training which result in the loss of privacy, a severely limited scale, a huge load on communications and a single point of failure. These constraints are even more deplorable in large and heterogeneous networks. To solve these issues, federated learning-based IDS models are suggested, but the existing practices fail to converge quickly, do not scale to non-IID data distributions and have an increased computation and communication cost which restricts its application. To overcome these issues, this paper proposes a Federated Enhanced Transformer-based Intrusion Detection System (FET-FIDS), a privacy-preserving and decentralized system of security, where federated learning is combined with Transformer-based self-attention. In the proposed architecture, a group of clients are introduced, each client is responsible for being trained on local network traffic data using FET-FIDS model. This method will help the system to learn intrusion patterns that are usually complicated to be learnt only in collaborative training. The locally trained model updates are then securely combined in a centralized server using adaptive federated averaging without having access to the raw data and, therefore, preserving their confidentiality of the data. The proposed architecture is effective in distributed and heterogeneous environments where under the experimental conditions taken into account in this study, its scalability, robustness and communication performance are improved. Using the provided means of wide-scale experimental analysis, the proposed FET-FIDS gives accuracy of 97.82%. It demonstrated that the proposed method is more effective, in terms of the detection, stability, and convergence behavior, than the existing centralized and federated IDS models. Further, it is shown that the framework can effectively deal with non-IID data distribution and the extensibility of the approach to different types of distributed network environment.