In order to know exactly how machine learning will benefit your organization against any security threats it faces, we must know exactly what machine learning is. Simply put, machine learning can be described as a computer’s ability to learn without it necessarily being programmed to. The computer will essentially create an algorithm, using mathematical techniques, by gathering data from huge datasets or behaviors and use it as a model basis for any future predictions it makes. One good example of this is Netflix’s ability to recommend you any new movies based on previous movies or series you have watched before.
Now that we know what machine learning is capable of doing, let’s go over the five ways you can implement it into your security.
1. Detect Malicious Activity or Stop Attacks
Since machine learning constantly monitors algorithms and creates its own, it’s no surprise that it could be used in your business to help detect malicious activity faster than it could be done manually, with it even going as far as stopping these attacks before they have started.
An example of this would be Enterprise Immune Solution, a machine learning-based firm that was able to detect and stop multiple cyber-attacks, the worst being the Wannacry ransomware attack. This attack alone affected more than 200,000 victims spanning 150 countries, but thanks to machine learning, it was detected within seconds.
2. Analyze Mobile Endpoints
Machine learning has been actively used in mobile devices for the last few years with the purpose of having an improved voice-based experience; an example of this would be Apple’s Siri, Amazon’s Alexa, or even Google Now. Yet it can be implemented into mobile security as well, with Google using machine learning to analyze any cyber threats against mobile endpoints.
3. Enhance Human Analysis
By using machine learning in security, we can make certain aspects of detection easier. One of these aspects is helping human analysts to detect malicious attacks, conduct vulnerability assessments, analyze a network, and cover endpoint protection.
In 2016 an adaptive machine learning security platform was created called AI by MIT’s Computer Science and Artificial Intelligence Lab. The AI was created to help analysts to find especially difficult-to-detect malicious software or just large amounts of data that would take analysts days to go over.
4. Automate Repetitive Tasks
Repetitive tasks are the bane of anyone’s existence; they take up more time and energy that could be spent productively somewhere else. By using machine learning, you can automate it to handle repetitive tasks so your employees can better spend their time doing other projects. By leaving low-value decision-making tasks to the computers, you’re able to deal with more strategic issues.
5. Close Zero-day Vulnerabilities
Amongst what we’ve already discussed above, machine learning can also be used for closing any vulnerabilities like zero-day threats or other malware that could target unsecured IoT devices. Arizona State University was able to monitor traffic on the dark web by using machine learning. They were able to identify data that related to zero-day attacks. This could be particularly useful for companies that want to close vulnerabilities and stop exploits before they do any damage.