SoK: A Taxonomy for Cybersecurity Incident Response Influence Factors
FH Münster University of Applied Sciences（明斯特应用科学大学）；Fraunhofer SIT and National Research Center for Applied Cybersecurity ATHENE（弗劳恩霍夫SIT和应用网络安全国家研究中心ATHENE）；Graduate School for Applied Research in North Rhine-Westphalia (Graduate School NRW)（北莱茵-威斯特法伦州应用研究研究生院）；International Computer Science Institute, UC Berkeley（加州大学伯克利分校国际计算机科学研究所）
Thomas Biege, Marius Brockhoff, Jonas Kaspereit, Fabian Ising, Lea Gröber, Sebastian Schinzel
Cybersecurity incident response has emerged as a critical area of interest for both researchers and practitioners. The corpus of literature on cybersecurity incident response is expanding, yet a unified framework for systematically organizing the accumulated knowledge remains absent. The aspects of incident response span multiple domains, including technology, human-computer interaction, organizational theory, and human factors. A comprehensive, integrative perspective on these factors can enable researchers to identify underexplored areas and more effectively target their empirical and theoretical investigations. Our study systematizes the factors that influence organizational preparedness for and response to cybersecurity incidents. Through a systematic review of academic literature (n = 417) and non-scientific publications (n = 40), we derived the "Cybersecurity Incident Response Influencing Factor Taxonomy" (\textit{CIR-IF Taxonomy}). Existing empirical findings were classified within this taxonomy, providing a comprehensive and up-to-date overview of knowledge from the period 1999 to mid-2024. The taxonomy categories were systematically compared with seven established scientific frameworks and with the \textit{NIST Cyber Security Framework} elements referenced in the \textit{NIST Special Publication 800-61r3} incident response profile. The results of this comparison show that the \textit{CIR-IF Taxonomy} delivers a richer, more rigorous, and more systematically organized view of the factors that drive and shape incident response.
SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity
King’s College London（伦敦国王学院）；The Alan Turing Institute（艾伦·图灵研究所）；University College London（伦敦大学学院）；Cardiff University（卡迪夫大学）；KU Leuven（鲁汶大学）；Devotion AI Labs（Devotion AI 实验室）
Taxonomy for Cybersecurity Threat Attributes and Countermeasures in Smart Manufacturing Systems
University of Massachusetts Dartmouth（麻省大学达特茅斯分校）；Western New England University（新英格兰西部大学）；University of South Carolina（南卡罗来纳大学）；The University of Arizona（亚利桑那大学）
SoK: Agentic Retrieval-Augmented Generation (RAG): Taxonomy, Architectures, Evaluation, and Research Directions
University of North Dakota（北达科他大学）；Youngstown State University（青年州大学）；University of Toledo（托莱多大学）；University of Missouri（密苏里大学）；Tribhuvan University（特里布文大学）
Quantum Machine Learning for Cybersecurity: A Taxonomy and Future Directions
Quantum Cloud Computing and Distributed Systems (qCLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne（量子云计算与分布式系统实验室，计算与信息系统学院，墨尔本大学）；Department of Computer Science and Engineering, Birla Institute of Technology and Science, Pilani, Pilani Campus, Vidya Vihar, Pilani, Rajasthan 333031, India（计算机科学与工程系，比拉理工学院，帕利尼校区，维迪亚维拉，帕利尼，拉贾斯坦邦333031，印度）；Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science, Pilani, Pilani Campus, Vidya Vihar, Pilani, Rajasthan 333031, India（电子与电气工程系，比拉理工学院，帕利尼校区，维迪亚维拉，帕利尼，拉贾斯坦邦333031，印度）；Department of Computer Science and Engineering, Birla Institute of Technology and Science, Pilani, Hyderabad campus, India（计算机科学与工程系，比拉理工学院，海得拉巴校区，印度）
University of Maryland College Park（马里兰大学帕克分校）；University of Virginia（弗吉尼亚大学）；Cornell University（康奈尔大学）；Columbia University（哥伦比亚大学）；University of California Los Angeles（加州大学洛杉矶分校）
