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Deep Learning

Solving Critical Workforce Challenges in the Aerospace & Defense Sector


Digital Transformation Solutions: The Key to Solving Critical Workforce Challenges in Aerospace & Defense The A&D industry, like many others, is facing severe talent shortages. So much so that by 2026, it’s predicted the industry will have 3.5 million job vacancies, and due to the skills gap, over half of them will not get filled. A reduced workforce, and one without the right skills or expertise, has hefty consequences for the A&D industry. Safety, compliance, and Aircraft on Ground (AOG) time are just a few of the areas impacted by the shortage. How can the industry overcome this challenge? We’ve created a whitepaper based on our expertise as a leader within workforce transformation solutions and the trusted provider to 8 of the top 10 A&D companies to help you navigate these waters. The whitepaper, titled “Digital Transformation Solutions: The Key to Solving Critical Workforce Challenges in Aerospace & Defense” provides insight and guidance on: · How digital transformation can help solve a (lack of) people problem · Common use cases of digital transformation solutions · Recommendations for deploying these solutions

A Method for Detecting Domain Generation Algorithms (DGAs) Using Deep Learning and Signal Processing Techniques

White Paper: ENSIGN

Background Domain Generation Algorithm, otherwise known as DGA, remains a potent technique used by cyber actors in their malware attacks. It begins with an automation programme designed to generate names of domains in a specific fashion, providing instructions and receiving information from malware. The use of DGAs allow attackers to quickly switch domains during malware attacks and circumvent traditional rule/signature-based security appliances aimed at blacklisting such malicious domains. Since DGAs are built and designed to generate thousands of domains, and remain active only for a limited period of time, efforts to tackle them could at times prove futile. Blacklisting a static list of malicious domains is no longer sufficient, given the unpredictable/non-static nature of a DGA, and the sheer volume of domains it uses. To address such attacks, we infuse machine learning and deep learning approaches into our advanced cyber analytics capabilities. These techniques facilitate the detection of elusive random domains generated by the malware when it attempts to connect to the attacker from a compromised host. Our proprietary DGA detection model possesses the ability to sieve through large traffic to ascertain the presence of DGA traits. It also determines if successful communications to malicious domains were made.

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