INFOBEYOND TECHNOLOGY LLC — Department of Defense SBIR Phase I: AF222-0012
INFOBEYOND TECHNOLOGY LLC — SBIR Phase I award from Department of Defense.
Phase I SBIR feasibility signal
- Phase I awards fund proof-of-concept work. For capture teams, they mark early interest from Department of Defense in a technical approach.
- Watch for Phase II follow-ons from the same firm/topic family — that conversion path is where budgets and transition pressure rise.
- Obligated amount $150,000. Cross-check similar awards in the same agency and technology tags for going-rate context.
- Topic code AF222-0012 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $150,000
- Agency
- Department of Defense · Air Force
- Program / Phase
- SBIR · Phase I
- Topic
- AF222-0012
- Solicitation
- 22.2
- NAICS
- —
- Place of performance
- KY
- Period
- 2022-12-12 → 2023-09-13
Description
Cybersecurity of avionic embedded systems in various U.S. Air Force (USAF) vehicles have become an urgent concern, due to the supply chain globalization. Supply chain vulnerabilities to an embedded system can be introduced anywhere from original equipment manufacturers, maintenance, repair, and overhaul providers. InfoBeyond advocates AutoGenMalware: Automate Zero-knowledge Behavior-centric Malware Sample Repository. AutoGenMalware is a comprehensive repository of the unforeseen effective malware test samples, which strengthens the anti-malware capability for avionics malware detection tools and quantitatively measures the effectiveness of cyber-resilient defense. Specifically, AutoGenMalware provides a unique and efficient Genetic Algorithm (GA) based malware generation framework by performing fitness analysis for each perturbation based on behavior and tracing rate. Based on the fitness result, ranked perturbations are chosen as elitism tournament method, and then the at least two variants are crossed over by the probability-weighted voting approach. Meanwhile, another malware mutation method dynamically introduces code caves in malware binaries, preserving their original functionality. By using GA based optimization and Deep-learning (DL) validation techniques, the most adequate content is then determined to place in such code caves for achieving misclassification. Finally, the effectiveness of the malware variants is evaluated and guaranteed through a quantitative scoring system along with the decision-making threshold.