The processing and storage of critical data in large-scale cloud networks necessitate the need for scalable security solutions. It has been shown that deploying all possible security measures incurs a cost on performance by using up valuable computing and networking resources which are the primary selling points for cloud service providers. Thus, there has been a recent interest in developing Moving Target Defense (MTD) mechanisms that helps one optimize the joint objective of maximizing security while ensuring that the impact on performance is minimized. Often, these techniques model the problem of multi-stage attacks by stealthy adversaries as a single-step attack detection game using graph connectivity measures as a heuristic to measure performance, thereby (1) losing out on valuable information that is inherently present in graph-theoretic models designed for large cloud networks, and (2) coming up with certain strategies that have asymmetric impacts on performance. In this work, we leverage knowledge in attack graphs of a cloud network in formulating a zero-sum Markov Game and use the Common Vulnerability Scoring System (CVSS) to come up with meaningful utility values for this game. Then, we show that the optimal strategy of placing detecting mechanisms against an adversary is equivalent to computing the mixed Min-max Equilibrium of the Markov Game. We compare the gains obtained by using our method to other techniques presently used in cloud network security, thereby showing its effectiveness. Finally, we highlight how the method was used for a small real-world cloud system.
Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges
School of Artificial Intelligence and Computer Science, Nantong University（人工智能与计算机科学学院，南通大学）；School of Electrical and Computer Engineering, The University of Sydney（电气与计算机工程学院，悉尼大学）；University of St Andrews（圣安德鲁大学）
A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective
Rensselaer Polytechnic Institute（伦斯勒理工学院）；Zhejiang University（浙江大学）；AntGroup（蚂蚁集团）；Ulsan National Institute of Science and Technology（蔚山国立科学技术院）；The University of Hong Kong（香港大学）
Towards Secure Retrieval-Augmented Generation: A Comprehensive Review of Threats, Defenses and Benchmarks
State Key Laboratory of Mathematical Engineering and Advanced Computing（数学工程与先进计算国家重点实验室）；Information Engineering University（信息工程大学）；Henan Key Laboratory of Information Security（河南省信息安全重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
Adversarial Attacks and Defenses in Large Language Models: Old and New Threats
Technical University of Munich（慕尼黑工业大学）；Munich Data Science Institute（慕尼黑数据科学研究所）；Université de Montréal（蒙特利尔大学）；Mila Quebec AI Institute（米拉魁北克人工智能研究所）
Security in the Fine-Tuning Lifecycle of Large Language Models: Threats, Defenses,Evaluation, and Future Directions
Hangzhou Normal University（杭州师范大学）；Zhejiang University（浙江大学）；China Mobile (Zhejiang) Innovation Research Institute Co., Ltd.（中国移动（浙江）创新研究院有限公司）；Quantum Cloud Computing and Distributed Systems (qCLOUDS) Lab, School of Computing and Information Systems（量子云计算与分布式系统（qCLOUDS）实验室，计算与信息学院）；The University of Melbourne（墨尔本大学）
Recent Advances in Attack and Defense Approaches of Large Language Models
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Xidian University（西安电子科技大学）；StepFun（阶跃星辰）；Megvii Technology（旷视科技）
MemPoison: Uncovering Persistent Memory Threats and Structural Blind Spots in LLM Agents
State Key Laboratory for Novel Software Technology, Nanjing University, China（南京大学新型软件技术国家重点实验室）；NARI Group Corporation/State Grid Electric Power Research Institute, China（国网电力研究院/国电电力集团）
SoK: The Last Line of Defense: On Backdoor Defense Evaluation
University of Bergen（卑尔根大学）；Radboud University（拉德博德大学）；Delft University of Technology（代尔夫特理工大学）；University of Pavia（帕维亚大学）；Ikerlan Research Center Radboud University（Ikerlan研究中心拉德博德大学）
Strategic Deflection: Defending LLMs from Logit Manipulation
International Artificial Intelligence Center of Morocco（摩洛哥国际人工智能中心）；Mohammed VI Polytechnic University（摩洛哥穆莱·伊斯梅尔理工学院）；AgriEdge；Sorbonne University, LIP6 - UMR 7606 CNRS（索邦大学，LIP6 - UMR 7606 CNRS）
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）
ShieldLearner: A New Paradigm for Jailbreak Attack Defense in LLMs
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Institute of Artificial Intelligence, Beijing University of Aeronautics and Astronautics（北京航空航天大学人工智能研究院）
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses
University of Maryland（马里兰大学）；Massachussetts Institute of Technology（麻省理工学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California, Berkeley（加利福尼亚大学伯克利分校）
A Novel Two-Staged Decision Support based Threat Evaluation and Weapon Assignment Algorithm, Asset-based Dynamic Weapon Scheduling using Artificial Intelligence Techinques
National University of Science and Technology（国家科技大学）
