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Machine Learning is one of the hottest topics in cybersecurity, stirring debate over whether computers can detect and stop cyber attacks faster than human analysts and conventional IT. Is a "rise of the machines" better able to protect corporate and personal information – or is it more hype than hope?

Machine Learning And Cybersecurity – Fact Or Fiction?

While it seems like the information security field has been using Machine Learning for years, in reality new machine learning technologies have only seen broad commercial adoption recently – but are making quite an impact. With more data scientists dedicating themselves to solving hard cybersecurity challenges, applications of machine learning to malware detection and software analysis are now closing the gaps left by signature-based antivirus – and stopping countless breaches every day.

Fact

The impact of machine learning in cybersecurity has taken off in recent years

The industry is far from ready to take humans "out of the loop" on most security operations and let machines run our cyber-defenses autonomously. However, machine learning breakthroughs are already making huge strides in reducing compromises as well as easing the burden of security staff in investigating suspicious events. These under-staffed teams are often overwhelmed with massive volumes of alerts, making it tough to weed out false alarms and identify the subtle signs of more sophisticated attacks.  
Machine learning anti-malware systems draw on deep knowledge of malware from analyzing millions of malicious samples. Cloud computing horsepower and data science techniques help master the "DNA" and "family trees" of malware, so even completely new malware is accurately detected.

Fiction

Machine learning is ready to replace human cybersecurity analysts

Machine learning based technologies can operate at high accuracy on inexpensive PCs using standard hardware. What’s required for success is "training" these algorithms on what malicious and benign software "look like," through granular analysis of millions of sample files. Once that has been done in the lab, a machine learning engine does not require significant computing resources to detect malware on end user devices.

Machine learning engines capable of stopping advanced malware can run on ordinary PCs

Machine learning breakthroughs in healthcare, entertainment and other fields are intriguing topics followed by cybersecurity researchers. However, major research and development is required to effectively apply machine learning insights from other fields to pressing cybersecurity problems, which pose their own unique set of challenges.

Machine learning breakthroughs in fields like audio processing and computer vision can be readily applied to cybersecurity

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