Machine Learning in Cybersecurity to Boost Big Data, Intelligence, and Analytics Spending to $96 Billion by 2021

Cyber threats are an ever-present danger to global economies and are projected to surpass the trillion dollar 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. 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,” says 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, or SIEM, and ultimately displace a large portion of traditional AV, heuristics, and signature-based systems within the next five years.”

ABI Research finds the government and defense, banking, and technology market sectors to be the primary drivers and adopters of machine learning technologies. User and Entity Behavioral Analytics (UEBA) along with Deep Learning algorithm designs are emerging as the two most prominent technologies in cybersecurity offerings, especially in innovative hot tech startups. 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 are expected to be separated altogether and integrated within different operations of UEBA, unsupervised, and deep learning solutions. Signature-based AV systems will be absorbed completely and comprise only a subsection of supervised machine learning models.

Enterprise-focused powerhouses 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,” concludes Pavlakis. “The proliferation of machine learning is also causing an explosion of agile startups, such as JASK, focusing more on SIEM complementary network traffic analysis and even pioneering application protection such as Sqreen.”

These findings are from ABI Research’s Machine Learning in Cybersecurity Technologies report.