Experts share best practices for data integrity, pattern recognition and computing power to help enterprises get the most out of machine learning-based technology for cybersecurity.
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The concept of machine learning has been around for decades. Machine Learning (ML) is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed.
Industries and government agencies working with large amounts of data are using machine learning technology to glean insights from this data in real time.
Financial institutions use the technology to identify investment opportunities and fraud. Utility companies use the technology to analyze sensor data to increase efficiency and save money. Healthcare practitioners are using the technology to identify trends that could improve diagnoses and patient treatment.
And, cybersecurity experts, inundated by reams of data generated by multiple information technology systems, security tools, networks, and other devices are deploying machine learning technology to detect and thwart internal and external cyber-attacks and threats.
“Machine learning helps humans be more efficient by [aggregating and analyzing] vast amounts of data.
It’s not just the volume, but also the scope of data; more data at the same time and more facets of data at the same time,” says Sven Krasser, chief scientist at Crowdstrike, a developer of machine learning-based endpoint security tools.
“One of the big game changers is the emergence of cloud computing,” he says. By using cloud-based infrastructures, security experts can aggregate more data from vast amounts of resources than ever before.” Traditional techniques where analysts sift through data in some manual fashion to generate rule sets doesn’t work well in today’s dynamically-changing threat environment, Krasser says.
System, sensors, and other networked-devices are generating so much data that it is increasingly difficult for human analysts to find those tidbits – the abnormalities and or patterns – that might give them the insights needed to identify an attack or potential threat, says Matt Wolff, chief data scientist with Cylance, a developer of endpoint security tools based on machine learning technology.
“So, machine learning is an excellent tool and the right approach to take when you have a data intensive problem that you want to solve,” Wolff says.
Industry executives and government agency officials are looking for ways to combat sophisticated attacks and relentless cyber adversaries while coping with a shortage of talented information security professionals. Machine learning-based security tools are yet another technology that they can add to their cyber arsenal.
DarkReading spoke with cybersecurity experts from CrowdStrike, Cylance, Darktrace, and IDC security researcher Peter Lindstrom to get a better sense of what organizations need to know about applying machine learning-based technology for cybersecurity in their organizations.
Rutrell Yasin has more than 30 years of experience writing about the application of information technology in business and government.
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