# Security Threats in Intelligent Transportation Systems and Their Risk Levels
## 5 Countermeasures  
In the following, we focus on the specific threats to ITS communications that we classify as critical. In addition to the solutions mentioned in the previous tables (Table 1 and 2), new countermeasures have been proposed in the literature to deal with the different critical attacks identified. In order to protect against Sybil Attack, Impersonation Attack and User Privacy disclosure, traditional countermeasures include session-key based mechanisms ([PERSON] et al., 2013) and public key infrastructure ([PERSON] and [PERSON], 2011). Among the recent solutions proposed, we cite the work proposed in [PERSON] et al. (2022). [PERSON] ([PERSON] et al., 2022) propose an approach based on signed time-stamped tags posted by roadside units(RSUs) as proof of the vehicle's anonymous location. The author proposes the execution of a proof-of-work (PoW) algorithm to prevent vehicles from setting multiple trajectories in the case of low-density RSUs. [PERSON] et al. (2017) present three SVM kernel functions-based classifiers to distinguish malicious nodes from benevolent nodes by measuring the deviation of their driving pattern matrices (DPMs). The proposed security services are implemented using three main techniques: encryption algorithms, public key infrastructure (PKI) and pseudonyms. The authors of [PERSON] et al. (2020) proposed a privacy-preserving detection scheme without the need for vehicles to disclose their infrastructure information by relying on a set of pseudonyms instead of assigning a specific identifier to each vehicle. In [PERSON] et al. (2019), the authors developed a solution to detect a Sybil attack based on the similarity of the movement paths of Sybil nodes, assuming that Sybil nodes always have the same position and movement paths, which is inconvenient and unsuitable in the real world. The solution detects a Sybil attack separately for each vehicle. [PERSON] et al. (2018) propose an approach based on computing trust in VANETs. The authors use the Bayesian neural network (BNN) model framework for predictive analysis, classification and node detection. Compared to a neural network (NN), a BNN keeps high performance by providing a robust distribution and the integration of the uncertain weights in the network. The solution developed in [PERSON] and Poniszewska-Maranda (2021) is based on time stamps and node identification information. A vehicle/node crosses an intersection each time with a given timestamp consisting of the current day and time. When the node arrives at another intersection, its timestamp is updated after checking whether the vehicle was able to travel the given path at the specified speed. To protect the network against the possibility of counterfeit tags, it must be possible to verify vehicle authenticity, for example by including a digital signature. In the same context, [PERSON] et al. (2017) suggest a cryptographic digital signature certificate method to set up trust between participating nodes. The asymmetric cryptography technique is used to combine the digital signatures. Each mobile vehicle in a VANET is allocated a set of public/private key pairs through which the vehicle identifies itself to the receivers by digitally signing the messages. The verification procedure is based on a local certificate session key. [PERSON] et al. (2017) propose three SVM kernel functions-based classifiers to discriminate malignant nodes from benevolent ones by assessing the divergence in their driving pattern matrices (DPMs). The proposed security services are based on three major mechanisms: encryption algorithms, public key Infrastructure (PKI) and pseudonymous. They evaluate vehicle driving patterns in neighborhood road traffic situations and consider the possibility of detecting Sybil attacks based on the variation of their driving patterns. The main intention is to estimate the resemblance of vehicle driving patterns, then use SVM classifiers to distinguish malicious nodes from benign ones.