Loren Data's SAM Daily™

SAMDAILY.US - ISSUE OF NOVEMBER 12, 2022 SAM #7652
SPECIAL NOTICE

70 -- Technology Licensing Opportunity for an Email Attachment and Link Scanning Device based on Semi-Supervised learning

Notice Date

11/10/2022 10:02:03 AM
 

Notice Type

Special Notice
 

NAICS

518210 — Data Processing, Hosting, and Related Services
 

Contracting Office

BATTELLE ENERGY ALLIANCE�DOE CNTR Idaho Falls ID 83415 USA
 

ZIP Code

83415
 

Solicitation Number

BA-1291
 

Response Due

11/30/2022 8:00:00 AM
 

Point of Contact

Andrew Rankin
 

E-Mail Address

andrew.rankin@inl.gov
(andrew.rankin@inl.gov)
 

Description

TECHNOLOGY LICENSING OPPORTUNITY Email Attachment and Link Scanning Device based on Semi-Supervised Learning A novel device using deep learning to identify malware documents and attachments accurately. Opportunity:�� Idaho National Laboratory (INL), managed and operated by Battelle Energy Alliance, LLC (BEA), is offering the opportunity to enter into a license and/or collaborative research agreement to commercialize this attachment and link scanning device. This technology transfer opportunity is part of a dedicated effort to convert government-funded research into job opportunities, businesses, and, ultimately, an improved way of life for the American people. Overview:������� This work addresses ransomware threats faced by small to midsize companies that cannot support extensive IT organizations to promote training or prevent spear phishing attacks. The average cost per incident for smaller companies is approximately $700,000. Deploying machine learning-based solutions has historically been computationally expensive. Competitors, including FireEye, binary defense, and carbon black, rely on cloud-based software solutions. Description:��� Researchers at Idaho National Lab have developed a device that uses the attention mechanism in deep learning to apply function names accurately and reliably to disassembled email attachments. Attachments or links classified as anomalous are then sequestered on the device in a virtual machine for revision, while attachments classified as non-anomalous are delivered to the user. The reverse-engineered function names and links are then sent to a probabilistic autoencoder for anomaly detection. ������������������������� The entire system is implemented in programmable logic on a device entirely independent of the user�s computer. The machine learning elements are implemented on field programmable gate array logic. The virtual machine running the operating system manages all emails intended for the user's system until released from the sequester and transferred to the host's system. Links or attachments flagged as anomalous are kept on the device and can be examined manually within the safety of the virtual machine without ever leaving the peripheral device. Benefits:��� ������ The key benefit is the use of programmable logic and on-premise deployment. Provides robust coverage of the Mitre attack matrix. The speed of the anomaly flagging process is on the millisecond timescale and enables support for monitoring multiple host systems. Applications:�� � Organizations that are vulnerable to ransomware and malware documents sent by email. Development Status:� TRL 3, currently undergoing proof-of-concept work. IP Status: ������� Patent Application No. 17/663,879, �Network Systems, Classification Methods, and Related Apparatuses for Security Analyses of Electronic Messages,� BEA Docket No. BA-1291. INL seeks to license the above intellectual property to a company with a demonstrated ability to bring such inventions to the market. Exclusive rights in defined fields of use may be available. Added value is placed on relationships with small businesses, start-up companies, and general entrepreneurship opportunities. Please visit Technology Deployment�s website at https://inl.gov/inl-initiatives/technology-deployment for more information on working with INL and the industrial partnering and technology transfer process. Companies interested in learning more about this licensing opportunity should contact Andrew Rankin at td@inl.gov.
 

Web Link

SAM.gov Permalink
(https://sam.gov/opp/3234e7ba437e4f318183d79457693951/view)
 

Place of Performance

Address: Idaho Falls, ID 83415, USA

Zip Code: 83415

Country: USA
 

Record

SN06514694-F 20221112/221110230100 (samdaily.us)
 

Source

SAM.gov Link to This Notice
(may not be valid after Archive Date)

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