Ethernet-Based IDS for Zonal Architecture

<div class="section abstract"><div class="htmlview paragraph">In an era where automotive technology is rapidly advancing towards autonomy and connectivity, the significance of Ethernet in ensuring automotive cybersecurity cannot be overstated. As vehicles increasingly rely on high-speed communication networks like Ethernet, the seamless exchange of information between various vehicle components becomes paramount. This paper introduces a pioneering approach to fortifying automotive security through the development of an Ethernet-Based Intrusion Detection System (IDS) tailored for zonal architecture.</div><div class="htmlview paragraph">Ethernet serves as the backbone for critical automotive applications such as advanced driver-assistance systems (ADAS), infotainment systems, and vehicle-to-everything (V2X) communication, necessitating high-bandwidth communication channels to support real-time data transmission. Additionally, the transition from traditional domain-based architectures to zonal architectures underscores Ethernet's role in facilitating efficient communication between different vehicle zones, thereby reducing wiring complexity and enhancing scalability.</div><div class="htmlview paragraph">However, the increasing adoption of Ethernet in automotive networks also brings forth new cybersecurity challenges. Ethernet-based communication introduces vulnerabilities that malicious actors can exploit to compromise vehicle systems' integrity and security. To address these challenges, robust cybersecurity measures are imperative.</div><div class="htmlview paragraph">This paper will introduce various sensors and methods designed to detect intrusions in Ethernet-based automotive architectures. Leveraging insights from the Automotive Open System Architecture (AUTOSAR) specification, innovative IDS models will be developed to monitor and counter threats targeting AUTOSAR diagnostics. These IDS models will utilize advanced detection mechanisms, including signature-based, anomaly-based, and machine-learning techniques, to swiftly identify and neutralize cyber threats in real-time.</div><div class="htmlview paragraph">By seamlessly integrating with AUTOSAR stacks and Ethernet communication protocols, these IDS models will offer heightened security, real-time threat detection, and adaptability to emerging threats. This research represents a significant advancement in automotive cybersecurity, emphasizing the importance of integrating robust security measures into the fabric of automotive technology to ensure the safety and reliability of autonomous and connected vehicles.</div></div>