Biblio

Edge computing for mobile devices in vehicular ad hoc networks (VANETs) has to address rogue edge attacks, in which a rogue edge node claims to be the serving edge in the vehicle to steal user secrets and help launch other attacks such as man-in-the-middle attacks. Rogue edge detection in VANETs is more challenging than the spoofing detection in indoor wireless networks due to the high mobility of onboard units (OBUs) and the large-scale network infrastructure with roadside units (RSUs). In this paper, we propose a physical (PHY)- layer rogue edge detection scheme for VANETs according to the shared ambient radio signals observed during the same moving trace of the mobile device and the serving edge in the same vehicle. In this scheme, the edge node under test has to send the physical properties of the ambient radio signals, including the received signal strength indicator (RSSI) of the ambient signals with the corresponding source media access control (MAC) address during a given time slot. The mobile device can choose to compare the received ambient signal properties and its own record or apply the RSSI of the received signals to detect rogue edge attacks, and determines test threshold in the detection. We adopt a reinforcement learning technique to enable the mobile device to achieve the optimal detection policy in the dynamic VANET without being aware of the VANET model and the attack model. Simulation results show that the Q-learning based detection scheme can significantly reduce the detection error rate and increase the utility compared with existing schemes.

Vehicular Ad Hoc Networks (VANETs) enable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications that bring many benefits and conveniences to improve the road safety and drive comfort in future transportation systems. Sybil attack is considered one of the most risky threats in VANETs since a Sybil attacker can generate multiple fake identities with false messages to severely impair the normal functions of safety-related applications. In this paper, we propose a novel Sybil attack detection method based on Received Signal Strength Indicator (RSSI), Voiceprint, to conduct a widely applicable, lightweight and full-distributed detection for VANETs. To avoid the inaccurate position estimation according to predefined radio propagation models in previous RSSI-based detection methods, Voiceprint adopts the RSSI time series as the vehicular speech and compares the similarity among all received time series. Voiceprint does not rely on any predefined radio propagation model, and conducts independent detection without the support of the centralized infrastructure. It has more accurate detection rate in different dynamic environments. Extensive simulations and real-world experiments demonstrate that the proposed Voiceprint is an effective method considering the cost, complexity and performance.

Primary user emulation (PUE) attack is one of the main threats affecting cognitive radio (CR) networks. The PUE can forge the same signal as the real primary user (PU) in order to use the licensed channel and cause deny of service (DoS). Therefore, it is important to locate the position of the PUE in order to stop and avoid any further attack. Several techniques have been proposed for localization, including the received signal strength indication RSSI, Triangulation, and Physical Network Layer Coding. However, the area surrounding the real PU is always affected by uncertainty. This uncertainty can be described as a lost (cost) function and conditional probability to be taken into consideration while proclaiming if a PU/PUE is the real PU or not. In this paper, we proposed a combination of a Bayesian model and trilateration technique. In the first part a trilateration technique is used to have a good approximation of the PUE position making use of the RSSI between the anchor nodes and the PU/PUE. In the second part, a Bayesian decision theory is used to claim the legitimacy of the PU based on the lost function and the conditional probability to help to determine the existence of the PUE attacker in the uncertainty area.