industrial control system operators

Abstract: The rapid growth of Distributed Energy Resources (DERs) has significantly expanded the cyber attack surface of modern power grids. Furthermore, increasing sophistication in attack techniques demands anomaly detection systems (ADS) that are accurate, interpretable, and reliable to support DER cybersecurity. While ML-based ADS provide strong detection capabilities, their black-box nature reduces operator trust and limits Security Operation Center's (SOC) ability to effectively interpret alerts and respond, highlighting the need for explainable Artificial Intelligence (XAI) to ensure transparency and operational confidence. This paper presents an XAI-based anomaly detection framework tailored for DER networks (ExCYDER). The proposed framework uses a self-verifying mechanism that validates ADS alerts to ensure trustworthy decision-making. ExCYDER combines LightGBM with SHAP to check whether each model decision aligns with its feature-attribution evidence, allowing the system to confirm that its internal reasoning is consistent and reliable. Experiments on a realistic DNP3 dataset achieved over 98% detection accuracy, an average rule--SHAP consistency of 44.6%, a SHAP latency of 14.5 ms per alert, and a confidence deviation within 5%, demonstrating stable verification behavior with minimal computational overhead. The framework distinguished between coherent and inconsistent alerts without compromising detection accuracy, demonstrating that integrated verification within XAI-based ADS enhances interpretability, auditability, and operational robustness for DER-focused SOCs.

Abstract: Cyber attacks are increasingly automated, narrowing the time available for human analysts to detect, reason about, and respond to intrusions. Large language models (LLMs) offer a promising foundation for autonomous cyber defense because they can correlate heterogeneous evidence and reason about previously unseen threats. However, directly applying LLMs to operational security telemetry is impractical: raw logs arrive faster than current models can process them, individual events are often ambiguous, and unconstrained LLM actions can introduce significant operational risk. We present BlueSTAR, a tiered agentic architecture for autonomous cyber defense in enterprise IT/OT networks. BlueSTAR first transforms high-volume security telemetry into compact indicators of compromise. We further introduce a resilience metric that jointly captures attacker reach, impact on mission-critical assets, and disruption caused by defensive actions. We evaluate BlueSTAR on two live enterprise IT/OT cyber ranges using seven attack chains based on real-world intrusion techniques. Across attack chains, BlueSTAR retains the fast containment of deterministic response for known threats while successfully defending against attacks requiring contextual and cross-cycle reasoning, including credential theft, repeated compromise, concurrent attackers, and attacks against physical processes.

Abstract: Zonal in-vehicle networks ship Ethernet, MACsec, and TSN, but treat the network itself as trusted: once configured at the factory, there is no standardized runtime way to easily revoke access, rotate keys, or contain a compromised ECU. Zero Trust Architecture targets exactly that gap, yet existing automotive ZTA proposals bolt on dedicated infrastructure that duplicates the SDN management plane already required to enable SDVs. Thus, ZTA is not yet adopted in the automotive domain, and the question remains: can we do better? We answer this in two steps. Step 1 analyses what Open Alliance TC17~v1.0 MACsec/MKA with pre-shared CAKs already provides in terms of NIST SP~800-207 ZTA tenets. Step 2 adds CORECONF/YANG management as proposed in Open Alliance TC19, maps the SDN Controller and Agents one-to-one onto NIST's PE, PA, and PEP. We then instantiate this with two YANG-based mechanisms: a network-access-control flow and a key-management scheme. The result fully covers five and two partially of the seven tenets with no ZTA-specific infrastructure added.