BLOCKCHAIN FOR DISTRIBUTED SYSTEMS SECURITY

Through support from Air Force Research Lab, we have conducted basic research on data provenance architecture for cloud using block chain, surveyed the vulnerabilities in block chain, in-depth analysis of the block discarding attack, developed a Proof-of-Stake consensus protocol for cloud based blockchain, architecture for secure BattlefieldIoT, cyber supply chain provenance, integration of software guard extensions on distributed ledgers for increased privacy. The team has published 14 articles in conference proceedings, three articles in journals, one magazine article and upcoming book published by IEEE-Press entitled, “Blockchain for Distributed Systems Security”. Since 2017, one research article, entitled “ProvChain: A Blockchain-based Data Provenance Architecture in Cloud Environment has garnered over 350 citations as of to date.

In addition to research articles, the team has developed three prototype tools. The Provchain tool provides Blockchain based data provenance capability in the cloud that allows cloud users to track the operations conducted on shared resources. A tool for Blockchain based information exchange provides an anonymous and privacy preserving mechanism to exchange cyber threat information. Finally, the tool for Blockchain based networked identity management provides the ability to detect rogue devices and unauthorized communication among authorized devices. We have disseminated the results of the project to Navy Research Lab, CERDEC, US Transcom, MITRE and Air Force Space Command. We also organized a symposium on Blockchain for Information sharing for NATO and shared the research results with the NATO community.

CYBER RESILIENCE FOR CRITICAL INFRASTRUCTURE

With support from the Department of Energy’s Cyber Resilient Energy Delivery consortium, I have ongoing research projects in developing cyber risk and resilience techniques. Specifically, the research objectives included: (a) Achieve balance between reducing security risk and cost of countermeasures in EDS, (b) Development of cyber defense remediation solution for EDS that takes into account operational resilience, (c) Develop metrics that characterizes attacker opportunity, capability and intent, (d) Autonomous attack containment during False Data Injection (FDI) attack. The work has resulted in over 20 publications in top tier conferences and journals. A software tool based on the research work is now being used by over 200 power utilities across 13 U.S. states on the East Coast.

BLOCKCHAIN FOR MEDICAL DEVICE SECURITY

Networking devices in healthcare facilities significantly aid the efficiency of day to day operations. As device numbers continue to rise, network congestion increases, impacting the quality of service for authorized devices and significantly increasing security risks and associated cost for healthcare facilities. To address the issue of operational efficiency and to mitigate security risks for connected devices, Sentara Healthcare and Old Dominion University have collaborated to create Bloxure (www.bloxure.com), a Blockchain powered device identity management platform to highlight the presence of rogue devices in Sentara Healthcare’s infrastructure. Bloxure serves to reduce network stress due to over-segmentation, to circumvent adversarial attacks that modify data and test results, gives access to real-time device monitoring, and automates response to attacks within a millisecond.

CYBER RISK SCORING AND MITIGATION

There is a need for tools to understand and quantify the security risk from cyber attacks within the critical infrastructure. Organizations need help in effective security risk assessment and management. The Cyber Risk Scoring and Mitigation (CRISM) tool employs advanced threat assessment techniques to assess risk posed by security vulnerabilities of an organization’s IT infrastructure. The tool provides a visual representation of the organization’s cybersecurity posture and provides recommendations for mitigating risks, allowing you to focus and prioritize your protective measures. The benefits of the tool will include: (a) Distilling the complex threat analysis processes into a single numerical risk score, (b) Ensures stakeholders comply with NIST cyber security requirements, (c) Provides organizations insights into risk posed by attacks originating at the perimeter vs. insider, (d) Provides a detailed, prioritized mitigation plan, (e) Employs an easy-to-use interface and advanced visualization techniques to ensure information synthesis.

RADIO FREQUENCY CLASSIFICATION FOR DRONE DETECTION

Through a CRADA with Naval Surface Warfare Center, Crane Indiana, we have developed machine learning based techniques to detect presence of drones based on classification of radio frequency signals. The technique has been implemented in a Software Defined Radio platform and has been tested at couple of drone ranges. We plan to extend this platform to explore additional security issues in next generation communication environments.