Smart Information Flow Technologies (SIFT) is a research and development consulting company specializing in Human Factors and Artificial Intelligence. SIFT's goal is to make the information flow between humans and technology better for both sides -- more efficient, productive, pleasant, and safer. In order to achieve this goal SIFT employs top engineers in the fields of Computer Science and Psychology who specialize in Human Computer Interaction, Interface Design, Human Performance, Artificial Intelligence, Network and Cybersecurity, and Politeness and Etiquette models.
Since our inception in 1999, SIFT personnel have extended the state of the art in a wide range of domains from commercial and military flight decks to DoD small unit operations and have authored well over a hundred papers documenting our many contributions to the state of the art in multiple fields.
Dr. Scott Friedman, Dr. David Musliner, and Mr. Jeffrey Rye published a journal article entitled "Improving Automated Cybersecurity by Generalizing Faults and Quantifying Patch Performance" in International Journal on Advances in Security, Volume 7, Number 3-4 (http://www.iariajournals.org/security/tocv7n34.html). The paper describes methods for automatically detecting and characterizing faults in production-quality software and then automatically generating and
evaluating patches. The methods are demonstrated on the Apache HTTP server and other Linux applications.
Ms. Peggy Wu will present SIFT's NASA funded work at the AAAI spring symposium series (http://www.aaai.org/Symposia/Spring/sss15.php) in a paper entitled Mining for Psycho-social Dimensions through Socio-linguistics. She will discuss results from an ongoing three year effort developing non-intrusive linguistic techniques for detecting psycho-social states in a Mars exploration simulation. March 22-23, Palo Alto, CA.
Drs. Ugur Kuter, Mark Burstein, J. Benton, Dan Bryce, Jordan Thayer and Steve McCoy will present their article HACKAR: Helpful Advice for Code Knowledge and Attack Resiliencein the Emerging Applications track of the AAAI Innovative Applications of AI (IAAI-15) conference at Austin TX. This paper describes our HACKAR system, a novel combination of Java program analysis and automated learning and planning architecture to the domain of Java vulnerability analysis. The key feature of HACKAR is its ability to analyze Java programs at development-time, identifying vulnerabilities and ways to avoid them. A prepublication draft is available on request from Dr. Kuter. The published paper will be available in the AAAI archives as well as Dr. Kuter's web site.