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Multi-Layer Mapping of Cyberspace

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-18-C-0416
Agency Tracking Number: N18A-019-0206
Amount: $124,979.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N18A-T019
Solicitation Number: 2018.0
Timeline
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-06-04
Award End Date (Contract End Date): 2018-12-04
Small Business Information
1236 E. Grant Rd
Tucson, AZ 85719
United States
DUNS: 009731002
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Salim Hariri
 (520) 829-6981
 salim@avirtek.com
Business Contact
 Salim Hariri
Phone: (520) 829-6981
Email: salim@avirtek.com
Research Institution
 The University of Arizona
 Gregory Ditzler
 
The University of Arizona
Tucson, AZ 85721
United States

 (520) 621-6180
 Nonprofit College or University
Abstract

Cyberspace includes a wide range of physical networks, storage and computing devices, applications, and users with different roles and requirements. These can be grouped into three layers: Physical, Logical, and Cyber-persona layers. Securing and protecting such complex and dynamic cyberspace resources and services are grand challenges. This project aims at developing a multi-layer anomaly behavior analysis of all components associated with each cyberspace layer and how they interact with each other in order to achieve superior capabilities in characterizing their normal operations and proactively detect any anomalous behavior that might be triggered by malicious attacks. In this project, we will develop a Multi-Layer Anomaly Behavior Analysis (MLABA) technology that is a true alternative to the existing security technologies. Our methodology to develop the proposed MLABA is based on a holistic approach that will continuously monitor, analyze, and diagnose the operations of all cyberspace layers in an integrated manner. For each layer, we will develop innovative data structures that can be used to accurately characterize the normal behavior of a resource, application or user to integrate the behavior of each layers to overcome the main limitations of anomaly behavior analysis and take proactive actions to stop cyberattacks propagation.

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

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