US Air Force Selects L3Harris Technologies to Develop Space Hub End Cryptographic Unit for Protected Tactical SATCOM Program

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  4 juin 2020 | International, C4ISR, Sécurité

  Watchdog says Pentagon needs better planning for IP update 17 years after first attempt

  Andrew Eversden A federal watchdog found that poor planning by the Department of Defense has blurred the department's understanding of the risks and costs associated with upgrading the system that routes internet traffic across the globe, known as Internet Protocol version 6 (IPv6). According to a June 1 report from the Government Accountability Office, the Pentagon needs to improve its transition planning for the most recent effort, which began in April 2017. The DoD has tried twice previously to implement IPv6 in 2003 and 2010, but stopped those transitions after identifying security risks and lacking adequately trained personnel. The problem for the DoD is that IPv4, the IP management system the DoD uses, is running out of address space. IPv4 only has room for 4.3 billion addresses. In contrast, IPv6, created in the 1990s, provides about 340,000,000,000,000,000,000,000,000,000,000,000,000 (undecillion) IP addresses. The Defense Department owns approximately 300 million IP addresses with about 59.8 million unused and planned for use by future DoD components. The department estimates it will run out of its unused IP addresses by 2030. The department's IPv6 implementation plan from early 2019 listed 35 actions needed to switch over from IPv4. Eighteen of those steps were scheduled to be completed by March 2020. The report said six of the 18 tasks were completed on time. Upgrading to IPv6 would increase connectivity, add security, improve the warfighter's connection and communications on the battlefield, and preserve interoperability with allied systems, the GAO wrote. The watchdog found that the department was not compliant with several IPv6 transition requirements from the White House's Office of Management and Budget. The DoD hasn't completed a cost estimate, developed a risk analysis or finished an inventory of IP compliant devices, the report said. Pentagon officials told the GAO that they knew their time frame for the transition was “optimistic," adding that they thought the pace was reasonable "until they started performing the work,” the GAO wrote. “Without an inventory, a cost estimate, or a risk analysis, DOD significantly reduced the probability that it could have developed a realistic transition schedule,” the GAO wrote. “Addressing these basic planning requirements would supply DOD with needed information that would enable the department to develop realistic, detailed, and informed transition plans and time frames.” The Department did meet OMB's requirement to name an official to lead and coordinate the agency planning. But because the Pentagon failed to complete the other three OMB requirements. the move is at risk. “Without an inventory, a cost estimate, or a risk analysis, DOD's plans have a high degree of uncertainty about the magnitude of work involved, the level of resources required, and the extent and nature of threats, including cybersecurity risks,” the GAO wrote. Among the DoD's goals it did complete are several IPv6 training programs, information sharing opportunities and a program management office. The GAO recommended that Defense Secretary Mark Esper direct the DoD chief information officer to complete an inventory of IP-compliant devices, develop a cost estimate and perform a risk analysis. The DoD agreed that it needed to develop a cost estimate and risk analysis but didn't concur that it needed to inventory devices, citing new guidance from OMB and calling an inventory “impractical” because of the department's size. “The lack of an inventory is problematic due to the role that it should play in developing transition requirements,” the GAO wrote. https://www.c4isrnet.com/it-networks/2020/06/02/watchdog-says-pentagon-needs-better-planning-for-ip-update-17-years-after-first-attempt/

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  23 mars 2020 | International, Terrestre, C4ISR

  Raytheon AI: Fix That Part Before It Breaks

  A modern mechanized military lives or dies by maintenance. But what if a computer could warn you when your weapons and vehicles were about to break, so you could fix them before they ever let you down? By SYDNEY J. FREEDBERG JR. WASHINGTON: Raytheon is working with the military on multiple pilot projects for AI-driven predictive maintenance. What's that? Traditionally, military mechanics spend a huge amount of time on what's called preventive maintenance: They carry truckloads of spare parts to war, they consult historical tables of roughly how often certain parts wear out or break down, and they preemptively crack open the access hatches to check those parts on a regular basis. The idea behind predictive maintenance is to feed all that historical data into a machine learning algorithm so it can tell maintainers, vehicle by vehicle and part by part, when something is likely to fail. It's a tremendous technical challenge that requires scanning in years of old handwritten maintenance forms, downloading digital records, and then constantly updating the database. Ideally, you want up-to-the-minute reports on things like engine temperature and suspension stress from diagnostic sensors installed in frontline vehicles. You need to account not only for what kind of equipment you're operating, but how hard it's running for a particular mission and even where in the world it's operating, because environmental conditions like heat, moisture, dust, and sand make a huge difference to wear and tear. And you can't just push out a single software solution and call it done. You have to constantly update your data so the algorithm can continue to learn, evolve, and adapt to different situations. But, Raytheon's Kevin Frazier and Butch Kievenaar told me, artificial intelligence and machine learning have advanced dramatically over just the last five years. Now Raytheon – a long-established defense contractor – is partnered with a flock of niche innovators to make it happen. Currently, they told me, Raytheon is already conducting or about to launch several multi-month pilot projects, seeking to prove the technology's value to the military: For the Army, they're working with a commercial partner on the M2 Bradley Infantry Fighting Vehicle, the mainstay armored troop transport of the heavy combat brigades, and the hulking M88 Hercules, a tracked “armored recovery vehicle” designed to tow broken-down battle tanks back for repair, if necessary under enemy fire. For the V-22 Joint Program Office – which supports the Osprey tiltrotor for the Marines, Air Force Special Operations Command, and now the Navy – they're working on the V-22's collision-avoidance radar, a Raytheon product. And across their customer base, they're looking at ways to do predictive maintenance on the many complex components Raytheon provides for a host of programs. How does this work? Let's hear from Kevin and Butch in their own words (edited for clarity and brevity from a highly technical 50-minute interview): Q: What kinds of problems can this technology help the military solve? Kevin: Right now, maintenance is conducted either on a scheduled timeline or when something breaks. What we are trying to do is replace that one piece because you know it's about to wear out and prevent it from breaking. Butch: One of the biggest things is you've got to understand what mission you're trying to achieve. If I'm trying to answer platform readiness questions, then I have to have certain data that's related to that topic. If I am trying to do supply chain analysis, I'm asking questions about where are critical parts and what size stockages we have to have to reduce turnaround time. So I'm answering a different question, and I'm looking at a different data set. So the key to setting all this up is what you do on the front end with your data to give the data scientists so that we can refine the algorithm appropriately. Q: AI/ML requires a lot of data. Is that data really available for all these different military systems? Kevin: It is. It's in different states. Some vehicles have sensors on them. Some do self-diagnostics. Some of the older equipment, especially the support equipment, doesn't have any sensors on them — but they all have files. They all are in the maintenance system, so the data exists. Data doesn't have to purely digital. It does have to be digitized at some point, but it doesn't necessarily have to start being digital. It could be maintenance logs that are hand-written, or the operator of a particular vehicle does a walk around and does an inspection report, writes that up — that's something that you actually can scan and input. Now we can add so many different types of data that your whole data environment becomes much richer. It helps you get to that algorithm — and then to continue to take in that data and refine that model. You're still recording that data and getting data from both handwritten and digital sources to update your model and tune it, so that you're just that much more accurate. Butch: What we're talking about is discrete algorithms solving for discrete problem sets. You look at the environment, and what the algorithm does is it learns. You keep ingesting data. You can get it a bunch of different ways so your analytical tool continues to learn, continues to refine. I can do a physical download from the vehicle, or scan maintenance records, or get it all fed off of a downloader that automatically feeds to the cloud. It can be as fast as we can automate the process of that piece of equipment feeding information back. For the Army and the Air Force especially, there is sufficient data over the last 15 that pertains to the impacts of combat. And we have it for different environments that you can then use to help train and refine the algorithms that you're using as it learns. Kevin: You have to understand the impacts the environment has on how the vehicle is functioning and what type of a mission you're doing, because that will cause different things to wear out sooner or break sooner. That's what the AI piece does. The small companies that we partner with, who are very good at these algorithms, already do this to some extent in the commercial world. We're trying to bring that to the military. Butch: The really smart data scientists are in a lot of the smaller niche companies that are doing this. We combine their tools with our ability to scale and wrap around the customer's needs. These are not huge challenges that we're talking about trying to solve. It is inside the current technological capability that exists. We have currently several pilot programs right now to demonstrate the use cases, that this capability that actually works. https://breakingdefense.com/2020/03/raytheon-ai-fix-that-part-before-it-breaks

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  21 septembre 2020 | International, Aérospatial

  Germany to form A400M Multinational Air Transport Unit with Hungary

  Gareth Jennings Germany is establishing a new multinational unit to operate the Airbus A400M transport aircraft, with Hungary committed as its first partner. The Bundeswehr announced on 17 September that the Multinational Air Transport Unit (MNAU) was being established at Wunstorf Air Base in northern Germany, home to the Luftwaffe's A400M force. This new international unit will relocate to Lechfeld Air Base in the far south of the country with 10 of the Luftwaffe's 53 contracted A400Ms, as well as additional A400Ms that might be provided by partner nations. Lieutenant General Ingo Gerhartz, inspector of the German Air Force, was quoted as saying, “The establishment of the implementation team of the Multinational Air Transport Unit is another milestone in multinational, military air transport. With the integration of the first technical officer of the Hungarian Air Force [HuAF], the cooperation with our Hungarian partners begins.” As noted by the Bundeswehr, the genesis of the MNAU dates back to 2017, when the Federal Ministry of Defence enabled the Luftwaffe to set up a new air transport association with multinational participation. With the Luftwaffe planning to field 40 A400Ms, this new multinational unit would operate all or some of the remaining 13 contracted to Germany. https://www.janes.com/defence-news/news-detail/germany-to-form-a400m-multinational-air-transport-unit-with-hungary