Shakil Ibne Ahsan | Searcharxiv

arXiv2024

An Explainable Ensemble-based Intrusion Detection System for Software-Defined Vehicle Ad-hoc Networks

Intrusion Detection Systems (IDS) are widely employed to detect and mitigate external network security events. Vehicle ad-hoc Networks (VANETs) continue to evolve, especially with developments related to Connected Autonomous Vehicles (CAVs). In this study, we explore the detection of cyber threats in vehicle networks through ensemble-based machine learning, to strengthen the performance of the learnt model compared to relying on a single model. We propose a model that uses Random Forest and CatBoost as our main investigators, with Logistic Regression used to then reason on their outputs to make a final decision. To further aid analysis, we use SHAP (SHapley Additive exPlanations) analysis to examine feature importance towards the final decision stage. We use the Vehicular Reference Misbehavior (VeReMi) dataset for our experimentation and observe that our approach improves classification accuracy, and results in fewer misclassifications compared to previous works. Overall, this layered approach to decision-making combining teamwork among models with an explainable view of why they act as they do can help to achieve a more reliable and easy-to-understand cyber security solution for smart transportation networks.

cs.CR↗

arXiv2024

Privacy-Preserving Intrusion Detection in Software-defined VANET using Federated Learning with BERT

The absence of robust security protocols renders the VANET (Vehicle ad-hoc Networks) network open to cyber threats by compromising passengers and road safety. Intrusion Detection Systems (IDS) are widely employed to detect network security threats. With vehicles' high mobility on the road and diverse environments, VANETs devise ever-changing network topologies, lack privacy and security, and have limited bandwidth efficiency. The absence of privacy precautions, End-to-End Encryption methods, and Local Data Processing systems in VANET also present many privacy and security difficulties. So, assessing whether a novel real-time processing IDS approach can be utilized for this emerging technology is crucial. The present study introduces a novel approach for intrusion detection using Federated Learning (FL) capabilities in conjunction with the BERT model for sequence classification (FL-BERT). The significance of data privacy is duly recognized. According to FL methodology, each client has its own local model and dataset. They train their models locally and then send the model's weights to the server. After aggregation, the server aggregates the weights from all clients to update a global model. After aggregation, the global model's weights are shared with the clients. This practice guarantees the secure storage of sensitive raw data on individual clients' devices, effectively protecting privacy. After conducting the federated learning procedure, we assessed our models' performance using a separate test dataset. The FL-BERT technique has yielded promising results, opening avenues for further investigation in this particular area of research. We reached the result of our approaches by comparing existing research works and found that FL-BERT is more effective for privacy and security concerns. Our results suggest that FL-BERT is a promising technique for enhancing attack detection.

cs.CR↗