Hybrid and Multifaceted Context-Aware Misbehavior Detection Model for Vehicular Ad Hoc Network

Ghaleb, Fuad A.; Maarof, Mohd Aizaini; Zainal, Anazida; Al‐rimy, Bander Ali Saleh; Saeed, Faisal; Al-Hadhrami, Tawfik

doi:10.1109/access.2019.2950805

Cited by 39 publications

(42 citation statements)

References 69 publications

(146 reference statements)

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“…As indicated by recent surveys [82], [83], the VANET solutions, in general, are mostly designed for use in Road-Side Units or a Central Authority and not in a backend system. To give an example, Ghaleb et al [84] introduce context-awareness in their solution. The authors consider the mobility information of surrounding vehicles in the VANET given by the internationally standardized basic safety messages (BSM) and cooperative awareness messages (CAM).…”

Section: A Fleet Securitymentioning

confidence: 99%

Context-Aware Security for Vehicles and Fleets: A Survey

2021

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Vehicles are becoming increasingly intelligent and connected. Interfaces for communication with the vehicle, such as WiFi and 5G, enable seamless integration into the user's life, but also cyber attacks on the vehicle. Therefore, research is working on in-vehicle countermeasures such as authentication, access controls, or intrusion detection. Recently, legal regulations have also become effective that require automobile manufacturers to set up a monitoring system for fleet-wide security analysis. The growing amount of software, networking, and the automation of driving create new challenges for security. Context-awareness, situational understanding, adaptive security, and threat intelligence are necessary to cope with these everincreasing risks. In-vehicle security should be adaptive to secure the car in an infinite number of (driving) situations. For fleet-wide analysis and alert triage, knowledge and understanding of the circumstances are required. Context-awareness, nonetheless, has been sparsely considered in the field of vehicle security. This work aims to be a precursor to context-aware, adaptive and intelligent security for vehicles and fleets. To this end, we provide a comprehensive literature review that analyzes the vehicular as well as related domains. Our survey is mainly characterized by the detailed analysis of the context information that is relevant for vehicle security in the future.

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Section: A Fleet Securitymentioning

confidence: 99%

Context-Aware Security for Vehicles and Fleets: A Survey

2021

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“…The communication between vehicles was simulated in MATLAB that closely follows the IEEE 802.11p/WAVE protocol specifications and assumptions as described in [35,36]. The used network simulator in this study was also used in many previous studies [28,37,38]. In the simulation, the transmission range was set to 1000 meters with a maximum broadcasting rate set to 10 messages per second.…”

Section: A Experimental Setupmentioning

confidence: 99%

Deep Kalman Neuro Fuzzy-Based Adaptive Broadcasting Scheme for Vehicular Ad Hoc Network: A Context-Aware Approach

Ghaleb

Al‐rimy²,

Almalawi

et al. 2020

IEEE Access

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Vehicular Ad Hoc Networks (VANETs) are among the main enablers for future Intelligent Transportation Systems (ITSs) as they facilitate information sharing, which improves road safety, traffic efficiency, and provides passengers' comfort. Due to the dynamic nature of VANETs, vehicles need to exchange the Cooperative Awareness Messages (CAMs) more frequently to maintain network agility and preserve applications' performance. However, in many situations, broadcasting at a high rate leads to congest the communication channel, rendering VANET unreliable. Existing broadcasting schemes designed for VANET use partial context variables to control the broadcasting rate. Additionally, CAMs uncertainty, which is context-dependent has been neglected and a predefined fixed certainty threshold has been used instead, which is not suitable for the highly dynamic context. Consequently, vehicles disseminate a high rate of unnecessary CAMs which degrades VANET performance. A good broadcasting scheme should accurately determine which and when CAMs are broadcasted. To this end, this study proposes a Context-Aware Adaptive Cooperative Awareness Messages Broadcasting Scheme (CA-ABS) using combinations of Adaptive Kalman Filter, Autoregression, and Sequential Deep Learning and Fuzzy inference system. Four context variables have been used to represent the vehicular context, namely, individual driving behaviors, CAMs uncertainty, vehicle density, and traffic flow. Kalman Filter and Autoregression are used to estimate and predict the CAMs messages respectively. The deep learning model has been constructed to estimate the CAMs' uncertainties which is an important context variable that has been neglected in the previous research. Fuzzy Inference System takes context variables as input and determines an accurate broadcasting threshold and broadcasting interval. Extensive simulations have been conducted to evaluate the proposed scheme. Results show that the proposed scheme improves the CAMs delivery ratio and decreases the CAMs prediction errors.

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“…One possible solution to address this issue is by combining the data-centric-based technique with behavioral techniques [67]. Such a combination is referred to as a hybrid-based approach in this paper.…”

Section: Related Workmentioning

confidence: 99%

“…However, the model was not evaluated against the context-aware and sophisticated attacks. Ghaleb and Maarof [67] proposed hybrid misbehavior detection that combines both behavioral-based classifiers with data-centric features. However, such an approach has overlocked the correlation between those multifaceted features causing a high false alarms rate.…”

Section: Related Workmentioning

confidence: 99%

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Ensemble-Based Hybrid Context-Aware Misbehavior Detection Model for Vehicular Ad Hoc Network

et al. 2019

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Life-saving decisions in vehicular ad hoc networks (VANETs) depend on the availability of highly accurate, up-to-date, and reliable data exchanged by neighboring vehicles. However, spreading inaccurate, unreliable, and false data by intruders create traffic illusions that may cause loss of lives and assets. Although several solutions for misbehavior detection have been proposed to address these issues, those solutions lack adequate representation and the adaptability to vehicular context. The use of predefined static thresholds and lack of comprehensive context representation have rendered the existing solutions limited to specific scenarios and attack types, which impedes their generalizability. This paper addresses these limitations by proposing an ensemble-based hybrid context-aware misbehavior detection system (EHCA-MDS) model. EHCA-MDS has been developed in four phases, as follows. The static thresholds have been replaced by dynamic ones created on the fly by analyzing the spatial and temporal properties of the mobility information collected from neighboring vehicles. Kalman filter-based algorithms were used to collect the mobility information of neighboring vehicles. Three sets of features were then derived, each of which has a different perspective, namely data consistency, data plausibility, and vehicle behavior. These features were used to construct a dynamic context reference using the Hampel filter. The Hampel-based z-score was used to evaluate the vehicles based on their behavioral activities, data consistency, and plausibility. For comprehensive features representation, multifaceted, non-parametric-based statistical classifiers were constructed and updated online using a Hampel filter-based algorithm. For accurate representation, the output of the statistical classifiers, vehicles' scores, context reference parameters, and the derived features were used as input to an ensemble learning-based algorithm. Such representation helps to identify the misbehaving vehicles more effectively. The proposed EHCA-MDS model was evaluated in the presence of different types of misbehaving vehicles under different context scenarios through extensive simulations, utilizing a real-world traffic dataset. The results show that the accuracy and robustness of the proposed EHCA-MDS under different vehicular dynamic context scenarios were higher than existing solutions, which confirms its feasibility and effectiveness to improve the performance of VANET critical applications. IntroductionRoad collisions are increasing, and they are being expected to be the fifth leading cause of death by 2030 [1,2]. Annually, millions of people lose their lives on roads worldwide due to traffic accidents [1], with 40 times more suffering from injuries. These accidents are also the main cause of traffic congestion, which in turn has a great impact on the economy [3,4], and billions of dollars are lost due to the treatment of injuries, loss of property, lost working hours, and high fuel consumption [5]. Several studies reveal that more than 95%...

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Hybrid and Multifaceted Context-Aware Misbehavior Detection Model for Vehicular Ad Hoc Network

Cited by 39 publications

References 69 publications

Context-Aware Security for Vehicles and Fleets: A Survey

Context-Aware Security for Vehicles and Fleets: A Survey

Deep Kalman Neuro Fuzzy-Based Adaptive Broadcasting Scheme for Vehicular Ad Hoc Network: A Context-Aware Approach

Ensemble-Based Hybrid Context-Aware Misbehavior Detection Model for Vehicular Ad Hoc Network

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