Defense Advanced Research Projects AgencyTagged Content List

Physical Security

Relating to the selective release of information and access to facilities

Showing 17 results for Security RSS
This month, DARPA will bring a demonstration version of a secure voting ballot box equipped with hardware defenses in development on the System Security Integrated Through Hardware and Firmware (SSITH) program to the DEF CON 2019 Voting Machine Hacking Village (Voting Village). The SSITH program is developing methodologies and design tools that enable the use of hardware advances to protect systems against software exploitation of hardware vulnerabilities. To evaluate progress on the program, DARPA is incorporating the secure processors researchers are developing into a secure voting ballot box and turning the system loose for public assessment by thousands of hackers and DEF CON community members.
January 23, 2019,
DARPA Conference Center
The Information Innovation Office is holding a Proposers Day meeting to provide information to potential proposers on the objectives of the new Guaranteed Architecture for Physical Security (GAPS) program. GAPS will develop hardware and software architectures that can provide physically provable guarantees around high-risk transactions, or where data moves between systems of different security levels. DARPA wants to ensure that these transactions are isolated and that the systems they move across are enabled with the necessary data security assertions. The intended outputs of this program are hardware and software co-design tools that allow data separation requirements to be defined during design, and protections that can be physically enforced at system runtime.
April 21, 2017,
Booz Allen Hamilton Conference Center
DARPA will host a Proposers Day in support of the System Security Integrated Through Hardware and Firmware (SSITH), on Friday, April 21, 2017 at the Booz Allen Hamilton Conference Center (3811 N. Fairfax Drive, Suite 600, Arlington, VA 22203) from 8:00am to 5:00pm EDT.
Researchers have demonstrated effective attacks on machine learning (ML) algorithms. These attacks can cause high-confidence misclassifications of input data, even if the attacker lacks detailed knowledge of the ML classifier algorithm and/or training data. Developing effective defenses against such attacks is essential if ML is to be used for defense, security, or health and safety applications.
Electronic system security has become an increasingly critical area of concern for the DoD and more broadly for security of the U.S. as a whole. Current efforts to provide electronic security largely rely on robust software development and integration. Present responses to hardware vulnerability attacks typically consist of developing and deploying patches to the software firewall without identifying or addressing the underlying hardware vulnerability. As a result, while a specific attack or vulnerability instance is defeated, creative programmers can develop new methods to exploit the remaining hardware vulnerability and a continuous cycle of exploitation, patching, and subsequent exploitations ensues.