SEARCH · Engineering Papers
Results for “Cyber”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Exploring the information content of cyber breach reports and the relationship to internal controls
Not Available
Quantifiable & comparable evaluations of cyber defensive capabilities: A survey & novel, unified approach
Not Available
Defending against cyber-attacks in building HVAC systems through energy performance evaluation using a physics-informed dynamic Bayesian network (PIDBN)
Not Available
Distributed optimal power flow and the multi-agent system for the realization in cyber-physical system
Not Available
Probabilistic extension of flexible hybrid state estimation for cyber-physical systems
Not Available
Detection of faulted phases in a medium-voltage main feeder using the cyber grid guard system with distributed ledger technology
Not Available
Blockchain-based automated and robust cyber security management
Not Available
Dynamic bayesian networks based abnormal event classifier for nuclear power plants in case of cyber security threats
Not Available
Strategy for distributed controller defence: Leveraging controller roles and control support groups to maintain or regain control in cyber-adversarial power systems
Distributed controllers play a prominent role in electric power grid operation. The coordinated failure or malfunction of these controllers is a serious threat, where the resulting mechanisms and consequences are not yet well-known and planned against. If certain controllers are maliciously compromised by an adversary, they can be manipulated to drive the system to an unsafe state. The authors present a strategy for distributed controller defence (SDCD) for improved grid tolerance under conditions of distributed controller compromise. The work of the authors’ first formalises the roles that distributed controllers play and their control support groups using controllability analysis techniques. With these formally defined roles and groups, the authors then present defence strategies for maintaining or regaining system control during such an attack. A general control response framework is presented here for the compromise or failure of distributed controllers using the remaining, operational set. The SDCD approach is successfully demonstrated with a 7-bus system and the IEEE 118-bus system for single and coordinated distributed controller compromise; the results indicate that SDCD is able to significantly reduce system stress and mitigate compromise consequences.
Assessing Cyber-Physical Risks of IoT-Based Energy Devices in Grid Operations
Not Available
The Virtualized Cyber-Physical Testbed for Machine Learning Anomaly Detection: A Wind Powered Grid Case Study
Not Available
Analyzing Cyber-Physical Modularity and Interdependence Using Bio-Inspired Graph Modeling
Not Available
Model-Free Cyber-Resilient Coordinated Inverter Control in a Microgrid
Not provided.
Autonomous Cyber Defense Against Dynamic Multi-strategy Infrastructural DDoS Attacks
Dynamic Infrastructural Distributed Denial of Service (I-DDoS) attacks constantly change attack vectors to congest core backhaul links and disrupt critical network availability while evading end-system defenses. To effectively counter these highly dynamic attacks, defense mechanisms need to exhibit adaptive decision strategies for real-time mitigation. This paper presents a novel Autonomous DDoS Defense framework that employs model-based reinforcement agents. The framework continuously learns attack strategies, predicts attack actions, and dynamically determines the optimal composition of defense tactics such as filtering, limiting, and rerouting for flow diversion. Our contributions include extending the underlying formulation of the Markov Decision Process (MDP) to address simultaneous DDoS attack and defense behavior, and accounting for environmental uncertainties. We also propose a fine-grained action mitigation approach robust to classification inaccuracies in Intrusion Detection Systems (IDS). Additionally, our reinforcement learning model demonstrates resilience against evasion and deceptive attacks. Evaluation experiments using real-world and simulated DDoS traces demonstrate that our autonomous defense framework ensures the delivery of approximately 96 - 98% of benign traffic despite the diverse range of attack strategies.