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TheSieve

Award Information
Agency: Department of Commerce
Branch: National Institute of Standards and Technology
Contract: 70NANB19H080
Agency Tracking Number: 053-03-02 (PII)
Amount: $368,134.55
Phase: Phase II
Program: SBIR
Solicitation Topic Code: None
Solicitation Number: N/A
Timeline
Solicitation Year: 2019
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-09-01
Award End Date (Contract End Date): 2021-08-31
Small Business Information
621 E. Pratt Street, Ste 610, Baltimore, MD, 21202
DUNS: 831438374
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Mark McLarnon
 (410) 779-6700
 mmclarnon@cyberpointllc.com
Business Contact
 Mark McLarnon
Phone: (410) 779-6700
Email: mmclarnon@cyberpointllc.com
Research Institution
N/A
Abstract
CyberPoint International presents the design of a cross platform product for the autonomous execution of live forensic investigations of Personal Computers, Laptops and Servers leveraging the NIST NSRL corpus and a combination of at least 3 forms of machine learning/artificial intelligent algorithms for the processing of preliminary digital evidence titled TheSieve. We build upon work from our phase one research effort to develop the suspicion score for a file based on an ensemble learning approach for features including entropy, location, size and file type. TheSieve will be a multi-tier product for conducting a live investigation requiring zero installation on target systems. TheSieve possesses the ability to automatically execute evidence collection and analysis techniques using a deterministic rule engine which fires during each step of analysis of a single host. Leveraging probability-based decision tree modeling, TheSieve will automatically offer suggestions on a target system under investigation at the end of collection and analysis. At the conclusion of this research effort, TheSieve will be a functional minimally viable product for conducting a live investigation of malicious code events or system misuse for Mac OS X and Linux endpoints and re-train data models based expert user feedback.

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

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