## 7 Discussion  
ITS is a complex multi-component system in which is sensible to cybersecurity and vulnerable in all its subsystems. In presented paper a four-layer model of IoT architecture that has been adapted to differentiate the issues more clearly is shown. **At perception** layer Spoofing attacks result in incorrect data acquisition. Denial-of-Service can cause failure of any of the systems. The main issue at this layer is configuration and initialization of the devices during manufacturing and internal vehicular network design, which does not comply with the connection of vehicles in dynamic networks. Security by contract concept is a promising technology at perception layer, especially with regard to issues related to changes and improvements in security strategies. Sensor fusion is successfully applied in practice in order to eliminate inaccurate information. **At network layer** numerous of cyberattacks are possible due to dynamic topology of the VANETs. Sometimes the attacking party can act passively, for example eavesdropping. Black and gray hole attacks omit the re-transmission of packets and thus disrupt communication. Man-in-the-Middle attack spread modified data. Timing attack delay transmission of the data and this way it damages systems that rely on real-time response. Sybil rely on replacing the identity of a nodes, thus can cause Jamming or Denial-of-Service. Due to the possibility of Identity attack the authentication of the nodes is necessarily to be anonymous. Different solutions with regard to anonymous authentication, are being sought to reduce the network and computing resources required for the continuous exchange of pseudonyms in VANET. One of the fastest growing technologies that is being experimented in this area is blockchain. In addition to anonymous authentication, blockchain in ITS security could find application in upper architecture layers as a secure data warehouse. Another answer to the question of reducing resources in anonymous authentication is Fog computing. Keeping the vulnerable identity information of the nodes at the edge of the system would limit the risk of attacks. The use of several complementary technologies is a possible solution to the issue of resource-effective authentication. A good example of this is a bloom filter as a main method and a blacklist and a request to the legitimate party as an auxiliary methods. At **support layer**, defense of fog-based structures is the main issue. Conventional security methods as cryptography and network segmentation are the most appropriate solution for Fog defense. They need to be adapted to the needs of ITS. At **application layer**, the main issues that security should focus on are description and standardization of the complex model of data and metadata and defense of systems, based on AI. The possible attacks at this layer are Data poisoning, Environmental Perturbations and Policy manipulation. Due to the complexity of ITS, an intelligent security strategy is required. AI, machine learning, ontologies and game theory are tools that have found application in cybersecurity solutions. Their application and adaptation to ITS needs to be studied in detail. Intelligent security often is based on cooperation between cybersecurity specialists and a variety of intelligent security solutions. An example of collaboration between experts and automated cybersecurity approaches is a system for cyber-risk scenario analysis for connected and automated vehicles (CAV) based on Bayesian Network (BN) presented in Reference [30]. In the initial phase of establishment, BN is constructed based on expert judgment. Quantitative and qualitative information from NVD (National Vulnerability) for 88,438 known vulnerabilities were used to refine BN, using machine learning methods. The performed tests demonstrate nearly 100% prediction accuracy of the quantitative risk score and qualitative risk level. Proposed methodology is applied to CAV GPS systems.