TARA Process Based on Data
OEMs may acquire more actual data from consumers' automobiles as contemporary vehicles increasingly communicate data with the cloud. TARA can benefit greatly from a big volume of data. For example, the TARA method, which is based on machine learning techniques, has extremely large data size needs. The accuracy of threat model training can be guaranteed with large-scale data. The data-driven TARA approach is a new research path.
TARA Methods that Take into Account Trade-Offs
The increased interaction and communication of cyber and automobile systems has created new safety and security problems.Because cyber-attacks might compromise a vehicle's functional safety, it's impossible to improve overall protection levels without including both sides. Furthermore, having too many security defensive measures would not only raise the overall vehicle cost, but it will also have an impact on the user experience. As a result, one essential aspect of TARA approaches is to evaluate the trade-offs of security, safety, vehicle cost, and user pleasure.
CONCLUSION
The probable security threats in linked autonomous vehicles are explored in this study, as well as the approaches of Threat Analysis and Risk Assessment in the automotive area, which are studied and contrasted. All of the approaches are categorized so that researchers may quickly and thoroughly grasp the topic of TARA. Additionally, the many ways to evaluate TARA methods in the literature are described. In addition, the future directions of TARA for the automotive area are explored.