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See detailFederated Learning-based Scheme for Detecting Passive Mobile Attackers in 5G Vehicular Edge Computing
Boualouache, Abdelwahab UL; Engel, Thomas UL

in Annals of Telecommunications (2021)

Detecting passive attacks is always considered difficult in vehicular networks. Passive attackers can eavesdrop on the wireless medium to collect beacons. These beacons can be exploited to track the ... [more ▼]

Detecting passive attacks is always considered difficult in vehicular networks. Passive attackers can eavesdrop on the wireless medium to collect beacons. These beacons can be exploited to track the positions of vehicles not only to violate their location privacy but also for criminal purposes. In this paper, we propose a novel federated learning-based scheme for detecting passive mobile attackers in 5G Vehicular Edge Computing. We first identify a set of strategies that can be used by attackers to efficiently track vehicles without being visually detected. We then build an efficient Machine Learning (ML) model to detect tracking attacks based only on the receiving beacons. Our scheme enables Federated Learning (FL) at the edge to ensure collaborative learning while preserving the privacy of vehicles. Moreover, FL clients use a semi-supervised learning approach to ensure accurate self-labeling. Our experiments demonstrate the effectiveness of our proposed scheme to detect passive mobile attackers quickly and with high accuracy. Indeed, only 20 received beacons are required to achieve 95\% accuracy. This accuracy can be achieved within 60 FL rounds using 5 FL clients in each FL round. The obtained results are also validated through simulations. [less ▲]

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See detailIntelligent Misbehavior Detection System for Detecting False Position Attacks in Vehicular Networks
Hawlader, Faisal UL; Boualouache, Abdelwahab UL; Faye, Sébastien UL et al

in Hawlader, Faisal; Boualouache, Abdelwahab; Faye, Sébastien (Eds.) et al The 2021 IEEE International Conference on Communications (the 4th Workshop on 5G and Beyond Wireless Security) (2021, June)

Position falsification attacks are one of the most dangerous internal attacks in vehicular networks. Several Machine Learning-based Misbehavior Detection Systems (ML-based MDSs) have recently been proposed ... [more ▼]

Position falsification attacks are one of the most dangerous internal attacks in vehicular networks. Several Machine Learning-based Misbehavior Detection Systems (ML-based MDSs) have recently been proposed to detect these attacks and mitigate their impact. However, existing ML-based MDSs require numerous features, which increases the computational time needed to detect attacks. In this context, this paper introduces a novel ML-based MDS for the early detection of position falsification attacks. Based only on received positions, our system provides real-time and accurate predictions. Our system is intensively trained and tested using a publicly available data set, while its validation is done by simulation. Six conventional classification algorithms are applied to estimate and construct the best model based on supervised learning. The results show that the proposed system can detect position falsification attacks with almost 100% accuracy. [less ▲]

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See detailSoftware-Defined Location Privacy Protection for Vehicular Networks
Boualouache, Abdelwahab UL; Soua, Ridha UL; Qiang, Tang et al

in Boualouache, Abdelwahab; Soua, Ridha; Qiang, Tang (Eds.) et al Machine Intelligence and Data Analytics for Sustainable Future Smart Cities (2021)

While the adoption of connected vehicles is growing, security and privacy concerns are still the key barriers raised by society. These concerns mandate automakers and standardization groups to propose ... [more ▼]

While the adoption of connected vehicles is growing, security and privacy concerns are still the key barriers raised by society. These concerns mandate automakers and standardization groups to propose convenient solutions for privacy preservation. One of the main proposed solutions is the use of Pseudonym-Changing Strategies (PCSs). However, ETSI has recently published a technical report which highlights the absence of standardized and efficient PCSs [1]. This alarming situation mandates an innovative shift in the way that the privacy of end-users is protected during their journey. Software Defined Networking (SDN) is emerging as a key 5G enabler to manage the network in a dynamic manner. SDN-enabled wireless networks are opening up new programmable and highly-flexible privacy-aware solutions. We exploit this paradigm to propose an innovative software-defined location privacy architecture for vehicular networks. The proposed architecture is context-aware, programmable, extensible, and able to encompass all existing and future pseudonym-changing strategies. To demonstrate the merit of our architecture, we consider a case study that involves four pseudonym-changing strategies, which we deploy over our architecture and compare with their static implementations. We also detail how the SDN controller dynamically switches between the strategies according to the context. [less ▲]

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See detailConsortium Blockchain for Cooperative Location Privacy Preservation in 5G-enabled Vehicular Fog Computing
Boualouache, Abdelwahab UL; Sedjelmaci, Hichem; Engel, Thomas UL

in IEEE Transactions on Vehicular Technology (2021)

Privacy is a key requirement for connected vehicles. Cooperation between vehicles is mandatory for achieving location privacy preservation. However, non-cooperative vehicles can be a big issue to achieve ... [more ▼]

Privacy is a key requirement for connected vehicles. Cooperation between vehicles is mandatory for achieving location privacy preservation. However, non-cooperative vehicles can be a big issue to achieve this objective. To this end, we propose a novel monetary incentive scheme for cooperative location privacy preservation in 5G-enabled Vehicular Fog Computing. This scheme leverages a consortium blockchain-enabled fog layer and smart contracts to ensure a trusted and secure cooperative Pseudonym Changing Processes (PCPs). We also propose optimized smart contracts to reduce the monetary costs of vehicles while providing more location privacy preservation. Moreover, a resilient and lightweight Utility-based Delegated Byzantine Fault Tolerance (U-DBFT) consensus protocol is proposed to ensure fast and reliable block mining and validation. The performance analysis shows that our scheme has effective incentive techniques to stimulate non-cooperative vehicles and provides optimal monetary cost management and secure, private, fast validation of blocks. [less ▲]

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See detailToward an SDN-based Data Collection Scheme for Vehicular Fog Computing
Boualouache, Abdelwahab UL; Soua, Ridha UL; Engel, Thomas UL

in IEEE International Conference on Communications ICC'2020 (2020, June 07)

With the integration of fog networks and vehicular networks, Vehicular Fog Computing (VFC) is a promising paradigm to efficiently collect data for improving safety, mobility, and driver experience during ... [more ▼]

With the integration of fog networks and vehicular networks, Vehicular Fog Computing (VFC) is a promising paradigm to efficiently collect data for improving safety, mobility, and driver experience during journeys. To this end, we exploit the Software-Defined Networking (SDN) paradigm to propose a fully-programmable, self-configurable, and context-aware data collection scheme for VFC. This scheme leverages a stochastic model to dynamically estimate the number of fog stations to be deployed. Our simulation results demonstrate that our proposed scheme provides lower latency and higher resiliency compared to classical data collection schemes. [less ▲]

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See detailSDN-based Misbehavior Detection System for Vehicular Networks
Boualouache, Abdelwahab UL; Soua, Ridha UL; Engel, Thomas UL

in 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) (2020, May)

Vehicular networks are vulnerable to a variety of internal attacks. Misbehavior Detection Systems (MDS) are preferred over the cryptography solutions to detect such attacks. However, the existing ... [more ▼]

Vehicular networks are vulnerable to a variety of internal attacks. Misbehavior Detection Systems (MDS) are preferred over the cryptography solutions to detect such attacks. However, the existing misbehavior detection systems are static and do not adapt to the context of vehicles. To this end, we exploit the Software-Defined Networking (SDN) paradigm to propose a context-aware MDS. Based on the context, our proposed system can tune security parameters to provide accurate detection with low false positives. Our system is Sybil attack-resistant and compliant with vehicular privacy standards. The simulation results show that, under different contexts, our system provides a high detection ratio and low false positives compared to a static MDS. [less ▲]

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See detailVPGA: an SDN-based Location Privacy Zones Placement Scheme for Vehicular Networks
Boualouache, Abdelwahab UL; Soua, Ridha UL; Engel, Thomas UL

in 38th IEEE International Performance Computing and Communications Conference (IPCCC) (2019, October 29)

Making personal data anonymous is crucial to ensure the adoption of connected vehicles. One of the privacy-sensitive information is location, which once revealed can be used by adversaries to track ... [more ▼]

Making personal data anonymous is crucial to ensure the adoption of connected vehicles. One of the privacy-sensitive information is location, which once revealed can be used by adversaries to track drivers during their journey. Vehicular Location Privacy Zones (VLPZs) is a promising approach to ensure unlinkability. These logical zones can be easily deployed over roadside infrastructures (RIs) such as gas station or electric charging stations. However, the placement optimization problem of VLPZs is NP-hard and thus an efficient allocation of VLPZs to these RIs is needed to avoid their overload and the degradation of the QoS provided within theses RIs. This work considers the optimal placement of the VLPZs and proposes a genetic-based algorithm in a software defined vehicular network to ensure minimized trajectory cost of involved vehicles and hence less consumption of their pseudonyms. The analytical evaluation shows that the proposed approach is cost-efficient and ensures a shorter response time. [less ▲]

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See detailSDN-based Pseudonym-Changing Strategy for Privacy Preservation in Vehicular Networks
Boualouache, Abdelwahab UL; Soua, Ridha UL; Engel, Thomas UL

in 15th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob'19) (2019, October)

The pseudonym-changing approach is the de-factolocation privacy solution proposed by security standards toensure that drivers are not tracked during their journey. SeveralPseudonym Changing Strategies ... [more ▼]

The pseudonym-changing approach is the de-factolocation privacy solution proposed by security standards toensure that drivers are not tracked during their journey. SeveralPseudonym Changing Strategies (PCSs) have been proposed tosynchronize Pseudonym Changing Processes (PCPs) between con-nected vehicles. However, most of the existing strategies are static,rigid and do not adapt to the vehicles’ context. In this paper, weexploit the Software Defined Network (SDN) paradigm to proposea context-aware pseudonym changing strategy (SDN-PCS) whereSDN controllers orchestrate the dynamic update of the securityparameters of the PCS. Simulation results demonstrate that SDN-PCS strategy outperforms typical static PCSs to perform efficientPCPs and protect the location privacy of vehicular network users [less ▲]

Detailed reference viewed: 246 (72 UL)