# Security Threats in Intelligent Transportation Systems and Their Risk Levels
## 5 Countermeasures
As a countermeasure for impersonation attacks between two authentic device-to-device (D2D) users, in [PERSON] et al. (2021), the authors propose a reinforcement learning-based technique that guarantees impersonator identification based on channel gains. They relate the performance of this technique in terms of false alarm rate, miss detection rate and average error rate. In [PERSON] and [PERSON] (2020), the authors present a comparison between K-nearest neighbors (KNN) and support vector machine learning algorithms to overcome impersonation attacks in VANETs. The experimental results showed that KNN gives better accuracy in detecting impersonation attacks compared to the SVM approach. In order to overcome impersonation attacks, [PERSON] et al. (2013) proposes a framework based on the cryptographic techniques to detect the impersonating node. Each node is given a unique identifier ID or pseudonym, and this information will be collected by a central authority to ensure confidentiality and privacy. Recently, two approaches have been used to provide anonymous services: group signature and pseudonymous authentication. Furthermore, there are hybrid methods that combine both group signing and pseudonymous authentication schemes. Both methods handle the problem of authentication and privacy. In group signature schemes, a vehicle receives a group private key with which it signs a message, whereas in pseudonymous authentication schemes ([PERSON] et al., 2018), individual vehicles store a set of identities. In [PERSON] et al. (2019), the authors propose certificate-less, aggregate-signature-based mechanism to perform message authentication without generating overhead for the system resources. The proposed approach uses the pre-computation method to minimize computation during the signature phase. More recently, in [PERSON] et al. (2021), a single-message cooperative authentication scheme based on certificate-less signatures is proposed. Several vehicles were randomly selected for new message authentication and construct the proof, which can be used for rapid message verification and is difficult to falsify. Flooding attacks, malware integration and blackhole attacks are critical attacks targeting availability. The key objective of these attacks is to inhibit ITS unit use and autonomous vehicle use of network facilities. These attacks can be initiated in the system by mischievous core or peripheral nodes. Several countermeasures have been proposed in the literature to mitigate flooding attacks, such as packet marking ([PERSON] et al., 2013), a trust model using transmission thresholds ([PERSON] and [PERSON], 2015) and monitoring SYN packets ([PERSON] et al., 2017). Recently, the authors of [PERSON] et al. (2018) proposed a hybrid intrusion detection system that enhances accuracy and other key performance metrics. The authors used a combination of artificial neural networks and a genetic algorithm and implemented two scenarios for computing performance metrics: misuse and anomaly. Moreover, in [PERSON] and [PERSON] (2019), the authors implement attacks such as flooding and blackhole using AODV routing protocol and improved AODV routing. Three different scenario were simulated and measured performance parameters such as end-to-end delay, packet overhead, packet delivery rate and packet drop ratio, which were analyzed and compared to existing protocols. Regarding malware integration, there are currently many types of malware, such as worms, computer viruses, ransomware, spyware, Trojans, rootkits, backdoors and botnets. Most malware works on computer systems or mobile devices. There are different infection vectors ([PERSON] and [PERSON], 2021; [PERSON] and [PERSON], 2019) depending on the characteristics of the specific malware. In [PERSON] et al. (2018), [PERSON] et al. proposed a two-layer model (named the coupled dynamic virus-traffic model) to study the propagation of viruses in V2V communication networks.