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Titlebook: Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems; Vipin Kumar Kukkala,Sudeep Pasricha Book 2023 The Edit

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樓主: 不幸的你
41#
發(fā)表于 2025-3-28 16:08:32 | 只看該作者
42#
發(fā)表于 2025-3-28 20:53:19 | 只看該作者
Secure by Design Autonomous Emergency Braking Systems in Accordance with ISO 21434emand since, without proper consideration, adversaries may become capable to remotely control vehicles endangering the life of occupants and bystanders. While several cybersecurity standards have recently emerged, such as the ISO 21434, the secure design of various automotive components is still cha
43#
發(fā)表于 2025-3-29 02:21:54 | 只看該作者
Resource Aware Synthesis of Automotive Security Primitivestures for transportation, power grids, smart buildings, and several other application domains. The implementation of most CPSs involves components like sensors, real-time computing platforms, actuators, networking primitives, etc. at the hardware level. Such an infrastructure needs to run real-time
44#
發(fā)表于 2025-3-29 03:19:46 | 只看該作者
Gradient-Free Adversarial Attacks on 3D Point Clouds from LiDAR Sensorsral networks are the state-of-the-art technique to classify 3D points from LiDAR sensors. However, neural networks have shown to be susceptible to adversarial attacks. In this article, we formalize these adversarial attacks on LiDAR semantic segmentation as generic multi-objective optimization and s
45#
發(fā)表于 2025-3-29 07:47:58 | 只看該作者
Internet of Vehicles: Security and Research Roadmapn transport systems. As the number of interconnected vehicles grows, new requirements for vehicular networks emerge. The original notion of vehicular ad-hoc networks (VANET) is morphing into a new concept known as the Internet of Vehicles (IoV). In this chapter, various challenges of secure automoti
46#
發(fā)表于 2025-3-29 14:27:04 | 只看該作者
Real-Time Intrusion Detection in Automotive Cyber-Physical Systems with Recurrent Autoencoders. The increasing efforts to make vehicles fully autonomous have led to high reliance on information from various external sources, which made the ECUs in the vehicles highly vulnerable to various cyber-attacks. Therefore, it is essential to have a robust detection system in vehicles that can detect
47#
發(fā)表于 2025-3-29 17:24:48 | 只看該作者
48#
發(fā)表于 2025-3-29 21:51:05 | 只看該作者
Deep AI for Anomaly Detection in Automotive Cyber-Physical Systems. The ever-increasing communication between ECUs and external electronic systems has made these vehicles particularly susceptible to a variety of cyber-attacks. In this chapter, we present a novel anomaly detection framework called TENET to detect anomalies induced by cyber-attacks on vehicles. TENE
49#
發(fā)表于 2025-3-30 03:47:45 | 只看該作者
50#
發(fā)表于 2025-3-30 06:31:58 | 只看該作者
Spatiotemporal Information Based Intrusion Detection Systems for In-Vehicle Networksis not a surprise that in-vehicle networks are exposed to numerous security threats. As vehicles are safety-critical, practical and effective steps should be taken to protect drivers and passengers. This chapter describes intrusion detection systems (IDS) on in-vehicle networks for reinforcing CAN s
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