Abstract
The world is expanding digitally at an ever-accelerating rate. As networks become larger and data becomes more complex, cyber security challenges are growing rapidly. To combat cyber attacks, machine learning (ML) and other artificial intelligence (AI) solutions should be utilised to design and build robust security solutions. With the explosion in the number of new techniques and frameworks in the ML/AI space, it is tricky for organisations to identify the best frameworks and approaches to build robust ML/AI solutions. In this article, an empirical analysis has been performed on various ML/AI frameworks to determine the performance and effectiveness of running ML/AI algorithms in a distributed manner.
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