The Controller Area Network (CAN) protocol, essential for automotive embedded systems, lacks inherent security features, making it vulnerable to cyber threats, especially with the rise of autonomous vehicles. Traditional security measures offer limited protection, such as payload encryption and message authentication. This paper presents a novel Intrusion Detection System (IDS) designed for the CAN environment, utilizing Hardware Performance Counters (HPCs) to detect anomalies indicative of cyber attacks. A RISC-V-based CAN receiver is simulated using the gem5 simulator, processing CAN frame payloads with AES-128 encryption as FreeRTOS tasks, which trigger distinct HPC responses. Key HPC features are optimized through data extraction and correlation analysis to enhance classification efficiency. Results indicate that this approach could significantly improve CAN security and address emerging challenges in automotive cybersecurity.

Modern vehicles are now part of a complex digital ecosystem, leveraging Artificial Intelligence (AI) and cloud computing for enhanced safety, efficiency, and user experience. However, this digital integration has introduced significant cy-bersecurity challenges, including data protection, unauthorized access prevention, and user privacy. As vehicles become more vulnerable to cyber-attacks, the industry must implement robust cybersecurity measures. Regulations like the UN’s UNR155 and UNR156 establish stringent cybersecurity requirements, demanding comprehensive manage-ment systems, regular updates, and continuous testing to counter evolving threats. These regulations under score the importance of cybersecurity in automotive safety. Future automotive cybersecurity will depend on developing advanced protections and collaboration among manufacturers,policymakers, and cybersecurity experts to ensure innovation and security in an interconnected digital world.

Modern vehicles are increasingly vulnerable to attacks that exploit network infrastructures, particularly the Controller Area Network (CAN) networks. To effectively counter such threats using contemporary tools like Intrusion Detection Systems (IDSs) based on data analysis and classification, large datasets of CAN messages become imperative.This paper delves into the feasibility of generating synthetic datasets by harnessing the modeling capabilities of simulation frameworks such as Simulink coupled with a robust representation of attack models to present CARACAS, a vehicular model, including component control via CAN messages and attack injection capabilities. CARACAS showcases the efficacy of this methodology, including a Battery Electric Vehicle (BEV) model, and focuses on attacks targeting torque control in two distinct scenarios.

The automotive market is profitable for cyberattacks with the constant shift toward interconnected vehicles. Electronic Control Units (ECUs) installed on cars often operate in a critical and hostile environment. Hence, both carmakers and governments have supported initiatives to mitigate risks and threats belonging to the automotive domain. The Local Interconnect Network (LIN) is one of the most used communication protocols in the automotive field. Today’s LIN buses have just a few light security mechanisms to assure integrity through Message Authentication Codes (MAC). However, several limitations with strong constraints make applying those techniques to LIN networks challenging, leaving several vehicles still unprotected. This paper presents LIN Multiplexed MAC (LIN-MM), a new approach for exploiting signal modulation to multiplex MAC data with standard LIN communication. LIN-MM allows for transmitting MAC payloads, maintaining full-back compatibility with all versions of the standard LIN protocol.

Interconnected devices are growing very fast in today's automotive market, providing new and complex features that cover very different domains. This vast and continuous requirement for new features brings to impact areas categorized as real-time safety-critical devices, opening the possibility to add potential vulnerabilities. By analyzing the security vulnerabilities within vehicle networks, this paper aims at proposing a new generation of a secure architecture based on Controller Area Network (CAN) called TAURUM P2T. This new architecture looks at mitigating the vulnerabilities found in the current network systems of road vehicles by introducing a low-cost and efficient solution based on the introduction of a Secure CAN network able to implement a novel key provisioning strategy. The proposed architecture has been implemented, resorting to a commercial Multi-Protocol Vehicle Interface module, and the obtained results experimentally demonstrate the approach's feasibility.

An algorithm for tracking multiple non-rigid objects in cluttered scenes is presented. The proposed approach models the shape of the objects by using corners. In particular, a learning algorithm is introduced in order to extract an adaptive model of the object automatically. The obtained adaptive model is used to individuate the object position and scale when occlusions are present. The method is used on an existing video-surveillance system in order to track moving objects in cluttered scenes. Results show that the proposed approach provides good performances with low processing times.

This paper discusses a typical architecture of a third-generation surveillance system (3GSS). In particular a method for choosing the optimal distribution of intelligence required by 3GSS is presented. Experimental results over a simulated system illustrate the presented approach.

In the past few years, the development of complex surveillance systems has captured the interest of both the research and industrial worlds. Strong and challenging requirements of modern society are involved in this problem, which aims to increase safety and security in several application domains such as transport, tourism, home and bank security, military applications, etc. At the same time, fast improvements in microelectronics, telecommunications, and computer science make it necessary to consider new perspectives in this field. The main objective of this paper is to investigate, discuss, and evaluate the impact of distributed processing and new communication techniques on multimedia surveillance systems, which represent the so-called third-generation surveillance systems (3 GSSs). In particular, aspects related to the distribution of intelligence among multiple-processing and wide-bandwidth resources are discussed in detail. It is shown how distribution of intelligence can be obtained by a hierarchical architecture that partitions, in a dynamic way, the main logical processing tasks (i.e., representation, recognition, and communication) performed in a 3 GSS physical architecture made up of intelligent cameras, hubs, and central control rooms. The advantages of this solution are pointed out in terms of 1) increased flexibility and reconfigurability and 2) optimal allocation of available processing and bandwidth resources. Finally, a case study is analyzed that allows one to gain a deeper insight into a distributed surveillance system.

An effective real-time post-processing algorithm for error recovery in noise corrupted JPEG bit streams integrated into an existing remote video-surveillance system is presented. The algorithm exploits information extracted by the video-surveillance system in order to detect corrupted frames and to recover them, enhancing the performances of the system, without compromising the real-time behavior of the application. Results show the validity of the presented approach.

This paper proposes a video-surveillance system based on a mobile camera. In particular the developed system creates (during the off-line phase) a panoramic multilayer background image allowing one to use common change detection algorithms to search for a change detection binary image. Different approaches to get the change detection images are presented. The performances of the implemented algorithms are presented by using ROC curves.

Tracking of non-rigid objects (e.g. humans) is a crucial application for understanding the behavior of objects. Different methods have been presented in literature, whose main drawback is low robustness or high computational load in analysis of cluttered scenes. In the paper a low computational algorithm for tracking non-rigid objects in cluttered scenes is presented. The proposed approach models the shape of the objects by using corners. A learning algorithm is introduced in order to automatically extract the model of the object from a short video sequence acquired immediately before merging of more objects in the scene. The adaptive model extraction mechanism strongly improves method robustness. The method is tested on an existing video-surveillance system in order to track moving objects in cluttered scenes. Results show that the proposed approach gives good performances with low-processing times.

Performance evaluation of image processing intermediate results in video based surveillance systems is extremely important due to the variety of approaches to this task. An approach based on the use of receiver operating characteristics (ROC) curves in order to evaluate the performance of a vision complex system for surveillance purposes is presented. The ROC curves have already been used in other research fields such as in the comparison of edge detection algorithms or in the evaluation of artificial neural networks: in this case they are used in order to compare different parameters selections within a system for the localization of moving objects. The presented results show the possibility of using ROC curves as a means for evaluation and comparison of video based surveillance systems.