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CRISIS: Knowledge Graph Based Cyber Resilience Integrated Security Inspection System

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-20-C-0792
Agency Tracking Number: N20A-T011-0185
Amount: $140,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N20A-T011
Solicitation Number: 20.A
Timeline
Solicitation Year: 2020
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-07-17
Award End Date (Contract End Date): 2021-01-13
Small Business Information
20271 Goldenrod Lane Suite 2066
Germantown, MD 20876-1111
United States
DUNS: 967349668
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Genshe Chen
 (301) 515-7261
 gchen@intfusiontech.com
Business Contact
 yingli Wu
Phone: (949) 596-0057
Email: yingliwu@intfusiontech.com
Research Institution
 George Mason University
 KC Chang
 
4400 University Drive
Fairfax, VA 22030-4422
United States

 (703) 993-1639
 Nonprofit college or university
Abstract

Modern US Navy ships and submarines are configured with an ever-increasing level of automation, including state-of-the-art embedded wireless sensors that monitor vital system functions. However, sensor nodes have the potential to serve as targets for cybersecurity attacks or be susceptible to corruption through accidental or malicious events. To address these shortfalls and minimize vulnerabilities of CBM+ systems, we propose to develop an integrated approach that includes both data-driven and model-based techniques to build a flexible and extensible cybersecurity layer incorporated into the CBMS for enhanced cyber resiliency. A cost-effective Cyber Resilience Integrated Security Inspection System (CRISIS) is proposed based on the Knowledge Graph (KG) and Deep Learning (DL) framework, which consists of three layers: input layer, knowledge layer, and reasoning layer. Input layer collects and processes dynamic knowledge to extract features based on condition-symptom relationships of machinery components and integrated system models. Knowledge layer develops a 2-levels low dynamic cyber resilience model (DeepDefense) with ML techniques and build a cybersecurity KG database. Reasoning layer implements the diagnostic and prognostic algorithms to derive a list of corresponding prioritized recommended attack detection and mitigation actions. A digital twin testbed is developed to create virtual models to support real-time system-aware cyber resilience monitoring.

* Information listed above is at the time of submission. *

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