Autonomous and connected vehicles are rapidly evolving, integrating numerous technologies and software. This progress, however, has made them appealing targets for cybersecurity attacks. As the risk of cyber threats escalates with this advancement, the focus is shifting from solely preventing these attacks to also mitigating their impact. Current solutions rely on vehicle security operation centers, where attack information is analyzed before deciding on a response strategy. However, this process can be time-consuming and faces scalability challenges, along with other issues stemming from vehicle connectivity. This paper proposes a dynamic intrusion response system integrated within the vehicle. This system enables the vehicle to respond to a variety of incidents almost instantly, thereby reducing the need for interaction with the vehicle security operation center. The system offers a comprehensive list of potential responses, a methodology for response evaluation, and various response selection methods. The proposed solution was implemented on an embedded platform. Two distinct cyberattack use cases served as the basis for evaluating the system. The evaluation highlights the system's adaptability, its ability to respond swiftly, its minimal memory footprint, and its capacity for dynamic system parameter adjustments. The proposed solution underscores the necessity and feasibility of incorporating dynamic response mechanisms in smart vehicles. This is a crucial factor in ensuring the safety and resilience of future smart mobility.
Flow Exporter Impact on Intelligent Intrusion Detection Systems
School of Engineering, Polytechnic of Porto (ISEP/IPP)（波尔图理工学院工程学院）；Porto Research, Technology & Innovation Center (PORTIC), Polytechnic of Porto (IPP)（波尔图理工学院波尔图研究、技术与创新中心）
Explainable AI-Driven Cyber Risk Analytics and Model Reliability Assessment for Intelligent Governance of U.S. Critical Infrastructure: An XGBoost and SHAP-Based Intrusion Detection Framework
Department of Business Information Systems, Central Michigan University（中央密歇根大学商业信息系统系）；Department of Information Studies, Trine University（特林大学信息学系）；Department of Computer Science, Maharishi International University（Maharishi国际大学计算机科学系）
Intelligent Green Efficiency for Intrusion Detection
Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD)（智能工程与先进创新发展计算研究组（GECAD））；School of Engineering, Polytechnic of Porto (ISEP/IPP)（波尔图理工学院工程学院（ISEP/IPP））
Elevating Intrusion Detection and Security Fortification in Intelligent Networks through Cutting-Edge Machine Learning Paradigms
Department of Computer Science and Technology, Beijing Institute of Technology（计算机科学与技术系，北京理工大学）；Department of Quantitative Methods and Economic Informatics, Faculty of Operation and Economics of Transport and Communication, University of Zilina（定量方法与经济信息学系，交通与通信运营经济学院，扎林大学）；Department of Telecommunications, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava（电信系，电气工程与计算机科学学院，奥斯特拉瓦技术大学）；Department of AI and Software, Gachon University（人工智能与软件系，高丽大学）；Department of Applied Informatics, Kimyo International University in Tashkent（应用信息学系，塔什干国际大学）
Machine Learning-Based Cyber Defense for Cloud Infrastructure: An Adaptive Deep Q-Network Architecture for Intelligent Intrusion Detection and Automated Threat Mitigation
Wilmington University（威尔明顿大学）；Washington University of Science and Technology（华盛顿科技大学）；South Dakota School of Mines & Technology（南达科他矿业理工学院）；South Dakota State University（南达科他州立大学）；St. Francis College（圣弗朗西斯学院）；Stanton University（斯坦顿大学）
Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework
Jilin University（吉林大学）；Nanyang Technological University（南洋理工大学）；Edith Cowan University（伊迪丝科文大学）；the University of Technology Sydney（悉尼科技大学）；The University of Sydney（悉尼大学）；Tohoku University（东北大学）
Securing the Digital World: Protecting smart infrastructures and digital industries with Artificial Intelligence (AI)-enabled malware and intrusion detection
LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion Detection
School of Computer Science and Technology, Harbin Institute of Technology（计算机科学与技术学院，哈尔滨工业大学）；Shandong Inspur Database Technology Co., Ltd（山东 Inspur 数据库技术有限公司）
Hybrid CNN-LSTM Framework for Intelligent Cyber Attack Detection and Prevention in U.S. Critical Digital Infrastructure: A Comparative Machine Learning Evaluation on CSE-CIC-IDS2018
Department of Computer Science, Maharishi International University（马哈拉吉国际大学计算机科学系）；Department of Information Studies, Trine University（特林大学信息学系）；Department of Business Information Systems, Central Michigan University（中央密歇根大学商业信息系统系）
Toward Intelligent Autonomous Agents for Cyber Defense: Report of the 2017 Workshop by the North Atlantic Treaty Organization (NATO) Research Group IST-152-RTG
