Universiti Teknologi Malaysia Institutional Repository

Hybrid and multifaceted context-aware misbehavior detection model for vehicular ad hoc network

Ghaleb, Fuad A. and Maarof, Mohd. Aizaini and Zainal, Anazida and Al-Rimy, Bander Ali Saleh and Saeed, Faisal and Al-Hadhrami, Tawfik (2019) Hybrid and multifaceted context-aware misbehavior detection model for vehicular ad hoc network. IEEE Access, 7 . pp. 159119-159140. ISSN 2169-3536

Full text not available from this repository.

Official URL: http://dx.doi.org/10.1109/ACCESS.2019.2950805


Vehicular Ad Hoc Networks (VANETs) have emerged mainly to improve road safety and traffic efficiency and provide user comfort. The performance of such networks' applications relies on the availability of accurate and recent mobility-information shared among vehicles. This means that misbehaving vehicles that share false mobility information can lead to catastrophic losses of life and property. However, the current solutions proposed to detect misbehaving vehicles are not able to cope with the dynamic vehicular context and the diverse cyber-Threats, leading to a decrease in detection accuracy and an increase in false alarms. This paper addresses these issues by proposing a Hybrid and Multifaceted Context-Aware Misbehavior Detection model (HCA-MDS), which consists of four phases: data-collection, context-representation, context-reference construction, and misbehavior detection. Data-centric and behavioral-detection-based features are derived to represent the vehicular context. An online and timely updated context-reference model is built using unsupervised nonparametric statistical methods, namely Kalman and Hampel filters, through analyzing the temporal and spatial correlation of the consistency between mobility information to adapt to the highly dynamic vehicular context. Vehicles' behaviors are evaluated locally and autonomously according to the consistency, plausibility, and reliability of their mobility information. The results from extensive simulations show that HCA-MDS outperforms existing solutions in increasing the detection rate by 38% and decreasing the false positive rate by 7%. These results demonstrate the effectiveness and robustness of the proposed HCA-MDS model to strengthen the security of VANET applications and protocols.

Item Type:Article
Uncontrolled Keywords:Hybrid, Kalman Filter
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ID Code:88215
Deposited By: Widya Wahid
Deposited On:15 Dec 2020 08:13
Last Modified:15 Dec 2020 08:13

Repository Staff Only: item control page