5 top machine learning use cases for security

Machine learning will make sense of the security threats your organization faces and help your staff focus on more valuable, strategic tasks. It could also be the answer to the next WannaCry.

artificial intelligence / machine learning / network
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At its simplest level, machine learning is defined as “the ability (for computers) to learn without being explicitly programmed.” Using mathematical techniques across huge datasets, machine learning algorithms essentially build models of behaviors and use those models as a basis for making future predictions based on newly input data. It is Netflix offering up new TV series based on your previous viewing history, and the self-driving car learning about road conditions from a near-miss with a pedestrian.

So, what are the machine learning applications in information security?

In principle, machine learning can help businesses better analyze threats and respond to attacks and security incidents. It could also help to automate more menial tasks previously carried out by stretched and sometimes under-skilled security teams.

Subsequently, machine learning in security is a fast-growing trend. Analysts at ABI Research estimate that machine learning in cyber security will boost spending in big data, artificial intelligence (AI) and analytics to $96 billion by 2021, while some of the world’s technology giants are already taking a stand to better protect their own customers.

Google is using machine learning to analyze threats against mobile endpoints running on Android -- as well as identifying and removing malware from infected handsets, while cloud infrastructure giant Amazon has acquired start-up harvest.AI and launched Macie, a service that uses machine learning to uncover, sort and classify data stored on the S3 cloud storage service.

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