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5 ways machine learning makes life harder for cybersecurity pros Posted on Aug 10 - 2018

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While many companies are turning to machine learning tools to fight hackers, they may not be as helpful as they seem thanks to a talent shortage and a lack of transparency.

By the end of 2017, some 61% of businesses had implemented artificial intelligence (AI) into their organizations—a 23% jump from the previous year, according to Narrative Science. And the incorporation of AI into business will only rise: The number of medium and large enterprises using machine learning is predicted to double by the end of 2018, said Deloitte.

Machine learning is a form of AI that interprets massive amounts of data, applying algorithms to the material, and making predictions off its observations. Common technologies that employ machine learning include facial recognition, speech recognition, translation services, and object recognition.

Businesses typically use machine learning for locating and processing large data sets that no human could sort through in a timely manner, if at all. Major companies like Amazon, IBM, Google, and Microsoft use machine learning to improve business functionality. But some organizations are implementing machine learning for more a narrow purpose: Cybersecurity.

While many assume machine learning makes cybersecurity professionals' lives much easier by better tracking security issues, that's not necessarily the case. Just like any new technology, machine learning still has its flaws—problems that turn the tech into more of a headache than a helping hand in the security space.

Here are the five ways machine learning may make things harder on cybersecurity pros.

1. Machine learning-equipped hackers

Machine learning can be helpful defending against attackers, but can be destructive when used by the wrong people. "An arms race is occurring as each side tries to one-up the other to make a better AI," said Ryan Ries, AI/machine learning expert at Onica.

Machine learning works faster than humans—a quality that is typically celebrated. However, not in the case of cyberattacking efforts.

"Human attackers will perform reconnaissance on a potential victim before launching a cyber attack, investigating things like what software they are running, the version of that software, any known vulnerabilities for said version, or any un-published zero day exploits shared among the hacker community that could improve their attack. This process can take many hours," said Emil Hozan, security analyst at WatchGuard Technologies. "But with machine learning, this research process can be carried out much more quickly and efficiently. Machine learning/AI hacking can also learn from past experiences; what didn't work on a similar previous hack attempt could be skipped over in favor of a new tactic." View More


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