DRIVE-B5G: A Flexible and Scalable Platform Testbed for B5G-V2X Networks

in DRIVE-B5G: A Flexible and Scalable Platform Testbed for B5G-V2X Networks (2022, December)

Unlike previous mobile networks, 5G and beyond (B5G) networks are expected to be the key enabler of various vertical industries such as eHealth, intelligent transportation, and Industrial IoT verticals ... [more ▼]

Unlike previous mobile networks, 5G and beyond (B5G) networks are expected to be the key enabler of various vertical industries such as eHealth, intelligent transportation, and Industrial IoT verticals. To support that, B5G networks enable to sharing of common physical resources (radio, computation, network) among different tenants, thanks to network slicing concept and network softwarization technologies, including Software Defined Networking (SDN) and Network Function Virtualization (NFV). Therefore, new research challenges related to B5G networks have emerged, such as resources management and orchestration, service chaining, security, and QoS management. However, there is a lack of a realistic platform enabling researchers to design and validate their solutions effectively, since B5G networks are still in their early stages. In this paper, we first discuss the different methods for deploying realistic B5G platforms for the V2X vertical, including the key B5G technologies. Then, we describe DRIVE-B5G, a novel platform that serves as an end-to-end test-bed to emulate a vehicular network environment, allowing researchers to provide proof of concept, validate, and evaluate their research approaches. [less ▲]

DRIVE-B5G: A Flexible and Scalable Platform Testbed for B5G-V2X Networks

in DRIVE-B5G: A Flexible and Scalable Platform Testbed for B5G-V2X Networks (2022, December)

Unlike previous mobile networks, 5G and beyond (B5G) networks are expected to be the key enabler of various vertical industries such as eHealth, intelligent transportation, and Industrial IoT verticals ... [more ▼]

Unlike previous mobile networks, 5G and beyond (B5G) networks are expected to be the key enabler of various vertical industries such as eHealth, intelligent transportation, and Industrial IoT verticals. To support that, B5G networks enable to sharing of common physical resources (radio, computation, network) among different tenants, thanks to network slicing concept and network softwarization technologies, including Software Defined Networking (SDN) and Network Function Virtualization (NFV). Therefore, new research challenges related to B5G networks have emerged, such as resources management and orchestration, service chaining, security, and QoS management. However, there is a lack of a realistic platform enabling researchers to design and validate their solutions effectively, since B5G networks are still in their early stages. In this paper, we first discuss the different methods for deploying realistic B5G platforms for the V2X vertical, including the key B5G technologies. Then, we describe DRIVE-B5G, a novel platform that serves as an end-to-end test-bed to emulate a vehicular network environment, allowing researchers to provide proof of concept, validate, and evaluate their research approaches. [less ▲]

Edge Computing-enabled Intrusion Detection for C-V2X Networks using Federated Learning

in The 2022 IEEE Global Communications Conference (GLOBECOM) (2022, December)

Intrusion detection systems (IDS) have already demonstrated their effectiveness in detecting various attacks in cellular vehicle-to-everything (C-V2X) networks, especially when using machine learning (ML ... [more ▼]

Intrusion detection systems (IDS) have already demonstrated their effectiveness in detecting various attacks in cellular vehicle-to-everything (C-V2X) networks, especially when using machine learning (ML) techniques. However, it has been shown that generating ML-based models in a centralized way consumes a massive quantity of network resources, such as CPU/memory and bandwidth, which may represent a critical issue in such networks. To avoid this problem, the new concept of Federated Learning (FL) emerged to build ML-based models in a distributed and collaborative way. In such an approach, the set of nodes, e.g., vehicles or gNodeB, collaborate to create a global ML model trained across these multiple decentralized nodes, each one with its respective data samples that are not shared with any other nodes. In this way, FL enables, on the one hand, data privacy since sharing data with a central location is not always feasible and, on the other hand, network overhead reduction. This paper designs a new IDS for C-V2X networks based on FL. It leverages edge computing to not only build a prediction model in a distributed way but also to enable low-latency intrusion detection. Moreover, we build our FL-based IDS on top of the well-known CIC-IDS2018 dataset, which includes the main network attacks. Noting that, we first perform feature engineering on the dataset using the ANOVA method to consider only the most informative features. Simulation results show the efficiency of our system compared to the existing solutions in terms of attack detection accuracy while reducing network resource consumption. [less ▲]

Federated Learning-based Inter-slice Attack Detection for 5G-V2X Sliced Networks

in Boualouache, Abdelwahab; Engel, Thomas (Eds.) 2022 IEEE 96th Vehicular Technology Conference: (VTC2022-Fall) (2022, September)

As a leading enabler of 5G, Network Slicing (NS) aims at creating multiple virtual networks on the same shared and programmable physical infrastructure. Integrated with 5G-Vehicle-to-Everything (V2X ... [more ▼]

As a leading enabler of 5G, Network Slicing (NS) aims at creating multiple virtual networks on the same shared and programmable physical infrastructure. Integrated with 5G-Vehicle-to-Everything (V2X) technology, NS enables various isolated 5G-V2X networks with different requirements such as autonomous driving and platooning. This combination has generated new attack surfaces against Connected and Automated Vehicles (CAVs), leading them to road hazards and putting users' lives in danger. More specifically, such attacks can either intra-slice targeting the internal service within each V2X Network Slice (V2X-NS) or inter-slice targeting the cross V2X-NSs and breaking the isolation between them. However, detecting such attacks is challenging, especially inter-slice V2X attacks where security mechanisms should maintain privacy preservation and NS isolation. To this end, this paper addresses detecting inter-slice V2X attacks. To do so, we leverage both Virtual Security as a Service (VSaS) concept and Deep learning (DL) together with Federated learning (FL) to deploy a set of DL-empowered security Virtual Network Functions (sVNFs) over V2X-NSs. Our privacy preservation scheme is hierarchical and supports FL-based collaborative learning. It also integrates a game-theory-based mechanism to motivate FL clients (CAVs) to provide high-quality DL local models. We train, validate, and test our scheme using a publicly available dataset. The results show our scheme's accuracy and efficiency in detecting inter-slice V2X attacks. [less ▲]

Federated Learning-based Scheme for Detecting Passive Mobile Attackers in 5G Vehicular Edge Computing

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 ▲]

A Trusted Hybrid Learning Approach to Secure Edge Computing

in IEEE Consumer Electronics Magazine (2021)

Securing edge computing has drawn much attention due to the vital role of edge computing in Fifth Generation (5G) wireless networks. Artificial Intelligence (AI) has been adopted to protect networks ... [more ▼]

Securing edge computing has drawn much attention due to the vital role of edge computing in Fifth Generation (5G) wireless networks. Artificial Intelligence (AI) has been adopted to protect networks against attackers targeting the connected edge devices or the wireless channel. However, the proposed detection mechanisms could generate a high false detection rate, especially against unknown attacks defined as zero-day threats. Thereby, we propose and conceive a new hybrid learning security framework that combines the expertise of security experts and the strength of machine learning to protect the edge computing network from known and unknown attacks, while minimizing the false detection rate. Moreover, to further decrease the number of false detections, a cyber security mechanism based on a Stackelberg game is used by the hybrid learning security engine (activated at each edge server) to assess the detection decisions provided by the neighboring security engines. [less ▲]

Consortium Blockchain for Cooperative Location Privacy Preservation in 5G-enabled Vehicular Fog Computing

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 ▲]

SDN-based Misbehavior Detection System for Vehicular Networks

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 ▲]

SDN-based Pseudonym-Changing Strategy for Privacy Preservation in Vehicular Networks

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 ▲]