30 juillet 2024 | International, Sécurité
US Department of Defense releases 2024 Arctic Strategy
The US and its allies seek to respond to heightened tensions in the Arctic with military preparedness and surveillance, avoiding escalation.
7 avril 2023 | International, Aérospatial
The Government Accountability Office rejected Sikorsky's protest of the Army's decision to choose Bell to build its Future Long Range Assault Aircraft.
30 juillet 2024 | International, Sécurité
The US and its allies seek to respond to heightened tensions in the Arctic with military preparedness and surveillance, avoiding escalation.
7 février 2019 | International, C4ISR
Today, machine learning (ML) is coming into its own, ready to serve mankind in a diverse array of applications – from highly efficient manufacturing, medicine and massive information analysis to self-driving transportation, and beyond. However, if misapplied, misused or subverted, ML holds the potential for great harm – this is the double-edged sword of machine learning. “Over the last decade, researchers have focused on realizing practical ML capable of accomplishing real-world tasks and making them more efficient,” said Dr. Hava Siegelmann, program manager in DARPA's Information Innovation Office (I2O). “We're already benefitting from that work, and rapidly incorporating ML into a number of enterprises. But, in a very real way, we've rushed ahead, paying little attention to vulnerabilities inherent in ML platforms – particularly in terms of altering, corrupting or deceiving these systems.” In a commonly cited example, ML used by a self-driving car was tricked by visual alterations to a stop sign. While a human viewing the altered sign would have no difficulty interpreting its meaning, the ML erroneously interpreted the stop sign as a 45 mph speed limit posting. In a real-world attack like this, the self-driving car would accelerate through the stop sign, potentially causing a disastrous outcome. This is just one of many recently discovered attacks applicable to virtually any ML application. To get ahead of this acute safety challenge, DARPA created the Guaranteeing AI Robustness against Deception (GARD) program. GARD aims to develop a new generation of defenses against adversarial deception attacks on ML models. Current defense efforts were designed to protect against specific, pre-defined adversarial attacks and, remained vulnerable to attacks outside their design parameters when tested. GARD seeks to approach ML defense differently – by developing broad-based defenses that address the numerous possible attacks in a given scenario. “There is a critical need for ML defense as the technology is increasingly incorporated into some of our most critical infrastructure. The GARD program seeks to prevent the chaos that could ensue in the near future when attack methodologies, now in their infancy, have matured to a more destructive level. We must ensure ML is safe and incapable of being deceived,” stated Siegelmann. GARD's novel response to adversarial AI will focus on three main objectives: 1) the development of theoretical foundations for defensible ML and a lexicon of new defense mechanisms based on them; 2) the creation and testing of defensible systems in a diverse range of settings; and 3) the construction of a new testbed for characterizing ML defensibility relative to threat scenarios. Through these interdependent program elements, GARD aims to create deception-resistant ML technologies with stringent criteria for evaluating their robustness. GARD will explore many research directions for potential defenses, including biology. “The kind of broad scenario-based defense we're looking to generate can be seen, for example, in the immune system, which identifies attacks, wins and remembers the attack to create a more effective response during future engagements,” said Siegelmann. GARD will work on addressing present needs, but is keeping future challenges in mind as well. The program will initially concentrate on state-of-the-art image-based ML, then progress to video, audio and more complex systems – including multi-sensor and multi-modality variations. It will also seek to address ML capable of predictions, decisions and adapting during its lifetime. A Proposers Day will be held on February 6, 2019, from 9:00 AM to 2:00 PM (EST) at the DARPA Conference Center, located at 675 N. Randolph Street, Arlington, Virginia, 22203 to provide greater detail about the GARD program's technical goals and challenges. Additional information will be available in the forthcoming Broad Agency Announcement, which will be posted to www.fbo.gov. https://www.darpa.mil/news-events/2019-02-06
19 novembre 2020 | International, Aérospatial, Naval
Posted on November 18, 2020 by Richard R. Burgess, Senior Editor ARLINGTON, Va. — The next generation of executive transport helicopter for the president of the United States is planned for Initial Operational Capability (IOC) in July 2021, a Navy spokeswoman said, but the decision of when to place the aircraft in service will be determined by the White House. The VH-92A, built by Sikorsky Aircraft Corp., a Lockheed Martin company, was selected in 2014 to replace the VH-3D and VH-60N helicopter fleet used to transport the president and other government executives. Six VH-92As were ordered in 2019. Followed by six more in February 2020. Total inventory will be 23 VH-92A aircraft, comprised of 21 operational fleet aircraft and two test aircraft. The presidential helicopter fleet is operated by Marine Helicopter Squadron One, based at Marine Corps Air Station Quantico, Va., with a detachment at Joint Base Anacostia-Bolling in Washington. “Government testing to validate system performance and prepare for Initial Operational Test and Evaluation is progressing on schedule and will support an Initial Operational Capability (IOC) planned for July 2021,” the Navy spokeswoman said. “The VH-92A will enter service post IOC at the determination of the White House Military Office.” https://seapowermagazine.org/marines-presidential-helicopter-headed-for-ioc-in-july/