You are here

4 - Trio System - AI/ML for Cybersecurity

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-20-F-0102
Agency Tracking Number: N193-A01-0014
Amount: $149,361.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N193-A01
Solicitation Number: 19.3
Timeline
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2019-11-21
Award End Date (Contract End Date): 2020-04-20
Small Business Information
25241 PASEO DE ALICIA STE 200
LAGUNA HILLS, CA 92653
United States
DUNS: 803826465
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Chieu Nguyen
 VP APPLIED RESEARCH
 (949) 273-5190
 CHIEU.NGUYEN@METRONOME-SOFTWARE.COM
Business Contact
 CHIEU NGUYEN
Phone: (949) 273-5190
Email: chieu.nguyen@metronome-software.com
Research Institution
N/A
Abstract

Cybercrimes are being committed constantly online against all persons, organizations and nations; becoming one of mankind’s greatest problems; threatening with cyber thefts of information, data, secrets; causing damages estimated in $ trillions; and creating new forms of extortions and crimes (e.g. ransomware). The same dangers could impact our nation’s federal and defense computing systems, which are at risk with grave consequences if their security is compromised. Unfortunately current countermeasures using conventional protection methods have difficulties keeping up with new, sophisticated and mutated forms of adversarial cyberattacks. The Phase-I project develops the Trio System for cybersecurity with advanced artificial-intelligence (AI) technologies that enable self-learning for absorbing relevant data, automating detections of cyber threats, known or mutated or newly devised, and empowering organizations, federal and defense departments to more effectively identify threats. The technologies to be developed include (classical) Machine Learning (ML) and Neural Network (NN) algorithms to provide a modern mechanism for the detection and prevention of cyber intrusions and anomalies in all enterprise and industrial-control-system networks. In addition, the developed self-learning and automation features can support the tasks of relieving cybersecurity personnel from having to sort through large sets of detection results, which presently can lead to “alert fatigue”.

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

US Flag An Official Website of the United States Government