The paper introduces ExCYDER, an explainable AI framework for anomaly detection in Distributed Energy Resource (DER) networks. It combines LightGBM with SHAP to self-verify alerts, ensuring that each detection aligns with feature‑attribution evidence. Experiments on a realistic DNP3 dataset show over 98% detection accuracy, 44.6% rule‑SHAP consistency, 14.5 ms SHAP latency per alert, and minimal confidence deviation, while distinguishing coherent from inconsistent alerts without sacrificing accuracy.
By Damilola Popoola, Souradeep Bhattacharya, Manimaran Govindarasu
arXiv:2608. 11679v1 Announce Type: new Abstract: Digital twins are increasingly used to monitor and simulate the behavior of cyber-physical systems.
By Touseef Hasan, Mounika Ghanta, Souvika Sarkar, Ujjwal Guin
arXiv:2606. 28923v1 Announce Type: new Abstract: Detecting security threats in an organization's cloud computing environment has become necessary due to the increased reliance on cloud infrastructure.
By Manu Nandan, TJ Jaymes, Michael Brautbar, Edward Raff
The article proposes a structured framework of behavioral indicators that could signal a progression toward potentially catastrophic threats from AI systems. Drawing on established methods from cybersecurity and national security, it defines clear metrics, indicators, and thresholds across multiple dimensions of AI capability and behavior. The framework is intended to enable researchers and policymakers to implement evidence‑based monitoring protocols for rogue AI progression.
By T. Bauer, W. P. Kegelmeyer, E. Begoli, A. Sadovnik, T. Emerson, C. Corley, N. Generous, J. Moore, B. Bartoldson, R. Goldhan, M. Goldman, M. Greaves, M. J. D. Vermeer, B. MacLennan, D. Schulker, N. VanHoudnos, J. Bansemer, Y. Bengio
arXiv:2603. 21194v2 Announce Type: replace-cross Abstract: Multi-agent discussions have been widely adopted, motivating growing efforts to develop attacks that expose their vulnerabilities.
By Qiuchi Xiang, Haoxuan Qu, Hossein Rahmani, Jun Liu
arXiv:2606. 31567v1 Announce Type: cross Abstract: Flaw reporting for deployed AI systems is fundamental to identifying system failures and improving AI safety.
By Shayne Longpre, Elaine Zhu, Carson Ezell, Avijit Ghosh, Sean McGregor, Kevin Paeth, Kevin Klyman, Sayash Kapoor, Rishi Bommasani, Ruth Appel, Gregory Strom, Lauren McIlvenny, Mark M. Jaycox, Peter Slattery, Nathan Butters, Arvind Narayanan, Percy Liang, Alex Pentland
arXiv:2602. 13807v2 Announce Type: replace Abstract: Time series anomaly detection is critical in many real-world applications, where effective solutions must localize anomalous regions and support reliable decision-making under complex settings.
By Xiaoyu Tao, Yuchong Wu, Mingyue Cheng, Ze Guo, Tian Gao
arXiv:2606. 01741v1 Announce Type: cross Abstract: Distributed event-based systems have become a common substrate for Internet-scale publish/subscribe services, IoT telemetry, cloud-native microservices, and security operations pipelines.
By Eric Liang
Distributed event-based systems have become a common substrate for Internet-scale publish/subscribe services, IoT telemetry, cloud-native microservices, and security operations pipelines. Their loose coupling and asynchronous delivery improve scalability, but they also expand the attack surface: publishers, brokers, subscribers, topics, schemas, and temporal ordering can each be abused without a single component observing the whole behavior.
arXiv:2606. 29721v1 Announce Type: cross Abstract: Maritime anomaly detection is essential for ensuring maritime safety, security, and efficient traffic management at sea, with Automatic Identification System (AIS) data serving as a primary data source.
By Youngseok Hwang, Sungho Bae, Dohun Lee, Jaeeun Seo, Jeehong Kim, Wonhee Lee, Hyunwoo Park
The paper discusses the need to adapt incident reporting frameworks for AI agents, which are rapidly deployed and face unique security challenges. By comparing AI systems and agents and consulting 23 experts, the authors identify key reporting elements such as agent memory, autonomy levels, and tool usage. They also highlight open research questions, potential reporting weaknesses like data leakage, and outline privacy requirements for secure AI agent deployment.
By Anastasia Pustozerova, Eugene Bagdasarian, Luca Beurer-Kellner, Battista Biggio, Nico Ebert, David Filip, Marc Fischer, Heather Frase, David Hofer, Juliane Hoffmann, Daphne Ippolito, Somesh Jha, Sean McGregor, Esfandiar Mohammadi, Luca Nannini, Cristina Nita-Rotaru, Alina Oprea, Kevin Paeth, Andrew Paverd, Jonathan Petit, Andreas Rauber, Christian Riess, John Sotiropoulos, Andreas Wespi, Kathrin Grosse
arXiv:2609.06063v1 Announce Type: new
Abstract: AI Agents are increasingly deployed in real-world settings, where they interact with external tools and make sequential decisions with limited human ov...
By Vittoria Vineis, Fabiano Veglianti, Lorenzo Antonelli, Claudia Di Carlo, Matteo Silvestri, Gabriele Tolomei