Cryptography and Security
3 papers, newest first.
-
PIA: Agentic Physics-Informed Runtime Anomaly Detection
Ali Mohammadi Ruzbahani, Abbas Yazdinejad, Hadis KarimipourRuntime anomaly detectors for cyber-physical systems are either learned as black boxes from normal data, which miss stealthy manipulations that keep every signal inside its normal range, or derived by hand from physical models, which requires expert effort for every plant. We introduce physics-informed agents (PIA), in which a large language model agent writes the detector itself as a set of physical residuals, such as pump characteristics, tank mass balances, and controller logic, and refines every candidate against normal operating data through an evaluation workbench before deployment. The result is a compact, human-readable detector that runs in milliseconds and names the component behind each alarm. On the BATADAL water distribution attack benchmark, PIA with Claude Opus 5.5 reaches an F1 of 0.878 on the test attacks against 0.504 for the strongest of six statistical and deep baselines, keeps detecting attacks concealed by replayed and offset sensor readings, and attributes every detected test attack to the attacked component. Ablations show that testing hypotheses against data is decisive: agents that write residuals from the plant description alone lose up to 0.28 F1, whereas agents that see only anonymized signals recover most of the physical structure.
-
Gather or Act? Belief-Level Counterfactual Labels for Deadline-Aware Incident Response in Cyber-Physical Systems
Ali Mohammadi RuzbahaniWhen an industrial control system misbehaves, the defender often must choose between acting now on the most likely cause and first gathering evidence that separates two look-alike causes, while the process drifts toward a shutdown. Learning or evaluating such decisions requires the cost of every candidate action from the same situation, which no public cyber-physical security dataset provides. We present TE-GvA, a counterfactual decision dataset for fault-driven incident response on the revised Tennessee Eastman process, with 9,908 decision points from 1,990 simulated incidents, each with 4 to 35 candidate actions labelled by branched rollouts with common random numbers. Labels computed under the true hidden cause (hindsight labels) make gathering the best choice in only 1.1-1.3% of decisions. For the dominant look-alike pair, a failed network link versus a frozen I/O module, we add pair-conditioned belief labels from twin episodes that replay the defender's history under the other cause, weighted by the exact posterior. With them, gathering is best in 15.5% of ambiguous natural decisions and in 33.1% of ambiguous stress decisions without a backup channel. When the posterior is correct and the twin is faithful, belief labels have the same regression target as hindsight labels and lower variance. In a pre-specified, single-look test evaluation, a gradient-boosted rule trained on belief labels has 11% lower regret on the stress set than the same rule trained on hindsight labels, and 27% lower regret on ambiguous stress decisions without a backup channel, which remains significant after Holm correction. With a backup channel and in the natural distribution, the two label sets perform about the same. Soft labels tolerate moderate posterior errors, whereas hard labels that commit to one cause do worse than hindsight labels.
-
Decentralizing Cybersecurity across Artificial Intelligence, Internet of Things, Blockchain, Machine Learning, and Agentic AI: A Systematic Literature Review
Rezvan AbouzadehCybersecurity was built around a perimeter, a trusted central server, and a human analyst who reads the alerts. None of the three holds up well in deployments where billions of constrained devices generate data at the edge, models are trained across organizational borders, ledgers record who did what, and autonomous software agents act at machine speed on behalf of people. This article reviews how decentralization is being used to respond to that shift. We code and synthesize 216 studies, 94 of them drawn from one research programme on federated, blockchain-based, and autonomous defence and 122 from the wider literature on mechanisms, attacks, and standards, across five technology families: the Internet of Things, machine learning, federated and other decentralized learning, blockchain, and agentic artificial intelligence. Four findings stand out. Decentralized learning is the most mature of the strands and now has a credible answer to poisoning and inference threats, though at a cost in complexity that few papers measure. Blockchains contribute auditability and identity more reliably than they contribute detection. Agentic AI is the least settled strand, with evidence of attack running well ahead of evidence of defence. And very few studies combine more than two families, so the integrated architectures the field keeps proposing are mostly untested. We distil three reusable algorithms and a layered reference architecture, and we close with a research agenda built around accountability, evaluation, and post-quantum readiness.