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Misconceptions about Machine Learning and Cybersecurity

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keysby Angela Guess

Sven Krasser and Dmitri Alperovitch recently wrote in Information Management, “Machine learning has never been more accessible than it is right now. Amazon utilizes it to uncover shopping habits and Netflix uses it to propose personalized movie selections… Leaders in the cybersecurity space are utilizing machine learning in a similar fashion. While there is a lot of buzz in the marketplace about the potential of the approach to solve persistent issues like silent failure and false positives, there are many misconceptions about how the technology is being applied in the field.”

Their list begins: “(1) Algorithms are Not Panacea. The value that machine learning can bring to the table largely depends on the data available to feed into it. Machine learning cannot create knowledge, it can only extract it. The scope and size of data are most critical for effective machine learning. For example, solutions that only analyze file contents easily fall prey to obfuscation techniques and will miss breaches that are purely exploitation-based and do not involve malware. Similarly, solutions that only consider behaviors observed on a single host or in a single sandbox are at a disadvantage to solutions that analyze behaviors in the cloud at large scale from a vast array of deployments.

They continue, “(2) Speed and Scale Matter. In order to analyze, swiftly and accurately, billions of events in real-time, machine learning models require a level of computational power and scalability that cannot be accomplished using old-school on-premise architecture and conventional database methods. Cloud-based architectures can significantly augment the efficacy of machine learning. Algorithms can be infused with the collective knowledge of a crowdsourced community where threat intelligence is aggregated and updated instantly. Identified attacks can then be turned into a new detection and learned by the algorithm, and shared with others within the cloud network to prevent the attack – sending the bad actors back to the drawing board.”

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