One of the key concerns related to the pervasive integration of artificial intelligence and machine learning (AI/ML) models in vehicular-to-everything (V2X) communication systems pertains to adversarial attacks, which may lead trained models to exhibit undesirable behaviors. As security and user safety are tightly coupled in V2X, ensuring the resilience of AI/ML models against adversaries becomes indispensable. However, addressing adversarial attacks poses a challenging task, requiring appropriate countermeasures to elevate the trustworthiness of the targeted AI/ML models. In this paper, we propose a deep reinforcement learning (DRL)-based approach to defend against two data poisoning attacks, namely label-flipping and policy induction. Extensive evaluation with the aid of an open-source dataset demonstrates that our scheme outperforms benchmark classifiers, achieving significantly superior detection performance in the presence of label-flipping attacks. The effectiveness of our DRL-based approach is also showcased under different adversarial strategies in the policy induction attack.

Emerging vehicle-to-everything (V2X) systems call for a diverse set of novel mechanisms to address vulnerabilities and security breaches. In this context, misbehavior detection approaches aim to detect malicious behavior of rogue V2X entities and possible attacks that may originate from them. In this paper, we introduce a data-driven ensemble framework which jointly leverages clustering and reinforcement learning to detect misbehaviors in unlabeled vehicular data. A rigorous detection assessment using an open-source dataset reveals meaningful performance trends for various attacks. In particular, while the majority of attacks can be effectively detected, detection may be curtailed for certain misbehavior types due to partly inaccurate clustering and erratic activity of the attacker over time. Performance comparison against benchmark detectors reveals the robustness of our approach in the presence of potentially inconsistent or mislabeled training data. The real-time detection capabilities of our framework are also explored in an effort to evaluate its practical feasibility in mission-critical V2X scenarios.

Vehicle-to-everything (V2X) communication is contributing towards the realization of futuristic vehicular networks such as Internet-of-Vehicles (IoV). The IoV is expected to usher in a new direction of intelligence and networking to achieve the goal of intelligent transport systems, which rely on the secure exchange of messages between vehicles and infrastructure. However, the transmission of false/incorrect data by malicious vehicles may cause serious damages on road safety. Therefore, it is crucial to detect safety-threatening incorrect information and mitigate potentially detrimental effects on road users. In this paper, we propose a reinforcement learning (RL)-based misbehavior detection approach for V2X scenarios. In our method, the RL-based detection model processes V2X data broadcast by vehicles as time-series at the roadside units, and classifies incoming data as misbehaving or genuine. We evaluate the proposed RL-based approach for detection of various attack types using an open-source dataset, and compare its performance against recent work in misbehavior detection. Our scheme is able to detect all types of misbehavior with a superior recall of 0.9970 and an F1 score of 0.9845, yielding a significant improvement over the benchmarks. Our research outcomes further reveal that misbehaving vehicles can be detected with a great accuracy of 0.9882 by exploiting real-time V2X information.

The transformative Internet-of-Vehicles (IoV) paradigm comes inadvertently with challenges which involve security vulnerabilities and privacy breaches. In this context, denial-of-service (DoS) attacks may perniciously affect the normal operation of IoV systems by causing extensive periods of network unavailability where legitimate vehicles are prevented from accessing vehicular services. In this paper, we offer an in-depth vulnerability assessment of 5G-enabled IoV systems when DoS attack variants are launched at multiple network domains. We further evaluate the resilience of an IoV-tailored authentication mechanism against DoS attacks under various configurations. A data-driven detection scheme is also proposed to address DoS variants in the radio access network, which take the form of false data injection attacks on the exchanged vehicular information. Our performance assessment with the aid of an open-source dataset reveals that the proposed scheme is able to accurately detect DoS traffic originated from malicious vehicles.

In order to improve road safety, modern vehicles are equipped with smart sensors and Vehicle-to-Everything (V2X) communication technologies that facilitate the exchange of data (e.g., location, speed, road hazards) with other vehicles, the road infrastructure, and pedestrians, thus extending the range of perception beyond the capabilities of on-board sensors. All these data can be processed by a Collision Avoidance service deployed in a mobile edge computing (MEC) platform to guarantee low latency in the detection and localization of road hazards. In this paper, we propose a Collision Avoidance service based on Vanetza, an open-source ETSI ITS protocol stack, and demonstrate its operation using already developed experimental On-Board Units (OBUs) that communicate with the Collision Avoidance service over UDP and MQTT to exchange ETSI ITS messages encoded in ASN.1 and JSON format, respectively. We measure the application-level latency and we observe the benefit of our proposed approach in terms of latency reduction by a factor of 12 with respect to the literature.

Highly dependable communication networks usually rely on some kind of Fast Re-Route (FRR) mechanism which allows to quickly re-route traffic upon failures, entirely in the data plane. This paper studies the design of FRR mechanisms for emerging reconfigurable switches. Our main contribution is an FRR primitive for programmable data planes, PURR, which provides low failover latency and high switch throughput, by avoiding packet recirculation. PURR tolerates multiple concurrent failures and comes with minimal memory requirements, ensuring compact forwarding tables, by unveiling an intriguing connection to classic “string theory” (i.e., stringology), and in particular, the shortest common supersequence problem. PURR is well-suited for high-speed match-action forwarding architectures (e.g., PISA) and supports the implementation of a broad variety of FRR mechanisms. Our simulations and prototype implementation (on an FPGA and a Tofino switch) show that PURR improves TCAM memory occupancy by a factor of 1.5 ×- 10.8 × compared to a naïve encoding when implementing state-of-the-art FRR mechanisms. PURR also improves the latency and throughput of datacenter traffic up to a factor of 2.8 ×- 5.5 × and 1.2 ×- 2 ×, respectively, compared to approaches based on recirculating packets.

Highly dependable communication networks usually rely on some kind of Fast Re-Route (FRR) mechanism which allows to quickly re-route traffic upon failures, entirely in the data plane. This paper studies the design of FRR mechanisms for emerging reconfigurable switches.