AILL-IDS: An Automatic Incremental Lifetime Learning Intrusion Detection System for Vehicular Ad Hoc Networks

Abstract

Vehicular Ad Hoc Networks (VANETs) play a critical role in enabling communication among intelligent vehicles, yet their dynamic and decentralized nature makes them highly vulnerable to cyber-attacks. Traditional Intrusion Detection Systems (IDSs) provide limited defense against these evolving threats, as they rely on static rules or machine learning (ML) models that lack the capacity for real-time updates. The Incremental Lifetime Learning IDS (ILL-IDS) was introduced to address this limitation by enabling adaptive learning of new attack types. However, ILL-IDS depends heavily on large volumes of high-quality labeled data, making the model update process costly and labor-intensive. In response, this study proposes the Automatic Incremental Lifetime Learning IDS (AILL-IDS), a novel IDS framework that significantly reduces the need for labeled data through incremental semi-supervised learning. This approach not only enables AILL-IDS to detect unknown attacks and adapt its model dynamically with minimal labeled data but also ensures continuous detection during the model update process, enhancing both speed and accuracy in threat detection. Experimental results demonstrate that AILL-IDS achieves a high detection rate of 0.97 and an average F1 score of 0.90, using only 5.5% labeled data, thereby offering an efficient and scalable solution for securing VANETs against emerging cyber threats.