Machine Learning a Security Growth Area

Tuesday, January 31, 2017 @ 04:01 PM gHale


Cyber threats are a growing danger to global economies and should jump over the $1 trillion mark in damages within the next year.

As a result, the cybersecurity industry is investing heavily in machine learning in hopes of providing a more dynamic deterrent.

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Along those lines, ABI Research forecasts machine learning in cybersecurity will boost Big Data, intelligence, and analytics spending to $96 billion by 2021.

“We are in the midst of an artificial intelligence security revolution,” said Dimitrios Pavlakis, industry analyst at ABI Research. “This will drive machine learning solutions to soon emerge as the new norm beyond Security Information and Event Management (SIEM) and ultimately displace a large portion of traditional AV, heuristics, and signature-based systems within the next five years.”

Government and defense, banking, and technology market sectors are the primary drivers and adopters of machine learning technologies, the researcher found.

User and Entity Behavioral Analytics (UEBA) along with Deep Learning algorithm designs are emerging as the two most prominent technologies in cybersecurity offerings.

Established antivirus (AV) players in the market, such as Symantec, continue to transform some of their solutions from highly trained supervised models to unsupervised and semi-supervised ones in preparation of the constantly shifting threat variables.

SIEM’s log-based methods may end up separated altogether and integrated within different operations of UEBA, unsupervised, and deep learning solutions. Signature-based AV systems will end up absorbed completely and comprise only a subsection of supervised machine learning models.

Enterprise-focused organizations like IBM will transform the way enterprises employ machine learning in every market sector, from healthcare to enterprise analytics to cybersecurity. Companies such as Gurucul, Niara, Splunk, StatusToday, Trudera, and Vectra Networks are attempting to take the lead in innovative applications of UEBA. Other market entrants like Deep Instinct and Spark Cognition are employing more feature-agnostic models, deep learning, and natural language processing.

“This radical transformation is already underway and is occurring as a response to the increasingly menacing nature of unknown threats and multiplicity of threat agents,” Pavlakis said. “The proliferation of machine learning is also causing an explosion of agile startups.”

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