L3Harris, Leidos collaborate on US Army’s ATHENA reconnaissance jet

On the same subject

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  June 22, 2018 | International, C4ISR

  The US made the wrong bet on radiofrequency, and now it could pay the price

  By: Aaron Mehta WASHINGTON – The Pentagon's belief in its technology drove the Department of Defense to trust it would have control over the electromagnetic spectrum for years to come, but that decision has left America vulnerable to new leaps in technology from China and Russia, according to a top military official. Gen. Paul Selva, vice chairman of the Joint Chiefs, has now concluded that the Pentagon needs to ensure it is keeping up with those near-peer nations, let along reestablishing dominance of electronic warfare and networking. “I think we assumed wrongly that encryption and our domination over the precision timing signals would allow us to evade the enemy in the electromagnetic spectrum. I think that was a bad assumption,” Selva said Thursday at the annual Center for a New American Security conference. “It's not that we disarmed, it's that we took a path that they have now figured out,” Selva said. China and Russia instead focused on deploying “digitally managed radio frequency manipulation, which changed the game in electronic warfare.” He added that a DoD study looking at the next decade concluded “We have some work to do.” Specifically, the United States needs to discover what Selva dubbed “alternative pathways” for communications and command and control systems. “It doesn't have to be a [radiofrequency] game. It's an RF game because we choose to make it so. And we're going to have to do some targeted investments in expanding the capacity of the networks that we use for command and control and battle management,” he said. “If we fail to do that, we're going to kick ourselves into the force-counterforce game inside the electromagnetic spectrum for the balance of the next couple of decades. “We have to adapt to that, and adapt quickly. The work has been done to characterize the problem, and the problem is, we're locked in this point-counterpoint fight with two potential competitors who have taken alternative paths. So we have to unlock a different way to do that work.” https://www.c4isrnet.com/it-networks/2018/06/21/the-us-made-the-wrong-bet-on-radiofrequency-and-now-it-could-pay-the-price/

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  December 31, 2018 | International, C4ISR

  US Spies Want to Know How to Spot Compromised AI

  BY DAVE GERSHGORN What if you were training an AI, and an adversary slipped a few altered images into its study set? The US government's research arm for intelligence organizations, IARPA, is looking for ideas on how to detect “Trojan” attacks on artificial intelligence, according to government procurement documents. Here's the problem the agency wants to solve: At a simple level, modern image-recognition AI learns from analyzing many images of an object. If you want to train an algorithm to detect pictures of a road signs, you have to supply it with pictures of different signs from all different angles. The algorithm learns the relationships between the pixels of the images, and how the structures and patterns of stop signs differ from those of speed-limit signs. But suppose that, during the AI-training phase, an adversary slipped a few extra images (Trojan horses) into your speed-limit-sign detector, ones showing stop signs with sticky notes on them. Now, if the adversary wants to trick your AI in the real world into thinking a stop sign is a speed-limit sign, it just has to put a sticky note on it. Imagine this in the world of autonomous cars; it could be a nightmare scenario. The kinds of tools that IARPA (Intelligence Advanced Research Projects Activity) wants would be able to detect issues or anomalies after the algorithm has been trained to recognize different objects in images. This isn't the only kind of attack on AI that's possible. Security researchers have also warned about inherent flaws in the way artificial intelligence perceives the world, making it possible to alter physical objects like stop signs to make AI algorithms miscategorize them without ever messing with how it was trained, called “adversarial examples.” While neither Trojan attacks nor the adversarial examples are known to have been used by malicious parties in the real world, researchers have said they're increasingly possible. IARPA is looking at a short timeline as well, expecting the program to conclude after a maximum of two years. https://www.defenseone.com/technology/2018/12/us-spies-want-know-how-spot-compromised-ai/153826

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  October 22, 2024 | International, C4ISR

  Northrop Grumman’s Deep Sensing and Targeting Technology Goes Airborne to Advance Vision for the US Army

  Aurora, Colo. – October 21, 2024 – Phase two of Northrop Grumman Corporation’s (NYSE: NOC) Deep Sensing and Targeting (DSaT) system was successfully demonstrated at Vanguard 24, an annual capstone...