A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
William Stallings
A comprehensive textbook survey of cryptography and network security principles and practice, covering both foundational concepts and modern applications.
Alex Kuo
This paper evaluates the opportunities and challenges of cloud computing in healthcare across management, technology, security, and legal aspects.
Tianyu Gu, Kang Liu, Brendan Dolan-Gavitt, et al.
This paper demonstrates that outsourced training of deep neural networks introduces security risks where adversaries can create backdoored networks that perform well on normal inputs but fail on attacker-chosen inputs.
Maanak Gupta, Charankumar Akiri, Kshitiz Aryal, et al.
This paper explores the dual-use of generative AI in cybersecurity, demonstrating attack techniques like jailbreaks and prompt injection, while also proposing defensive applications.
Joseph Gardiner, Shishir Nagaraja
This paper systematizes command and control (C&C) detection techniques and analyzes their resilience to evasion attacks on machine learning components.
Antonio Emanuele Ciná, Kathrin Grosse, Ambra Demontis, et al.
A comprehensive survey systematizing 15 years of poisoning attacks and defenses in machine learning, with a focus on computer vision.
S. M. Riazul Islam, Daehan Kwak, Md. Humaun Kabir, et al.
This paper surveys IoT-based healthcare technologies, architectures, applications, security, and policies, proposing an intelligent collaborative security model.
Dezhang Kong, Shi Lin, Zhenhua Xu, et al.
A comprehensive survey of security risks in LLM-driven AI agent communication, proposing a three-class framework and analyzing protocols like MCP and A2A.
Yuanchun Li, Hao Wen, Weijun Wang, et al.
This paper surveys and analyzes Personal LLM Agents, focusing on their capability, efficiency, and security to guide future development.
Unknown
A survey of security challenges for AI agents, identifying four knowledge gaps and mapping them to key threats and future pathways.
Vikas Hassija, Vinay Chamola, Vikas Saxena, et al.
This survey reviews IoT security challenges and solutions, focusing on blockchain, fog computing, edge computing, and machine learning to achieve end-to-end secure IoT environments.
Mingfu Xue, Chengxiang Yuan, Heyi Wu, et al.
A comprehensive survey of machine learning security covering threats, countermeasures, and evaluations across training and test phases.