At its simplest level, machine learning is defined as “the ability to learn without programming for computers”. Using mathematical techniques in large data sets, machine learning algorithms mainly create behavioral models and use these models as a basis in the future.
So, what are the machine learning applications in the field of information security?
In principle, machine learning can help businesses better analyze threats and react to attacks and security incidents. It can also help automate tasks previously performed by security teams.
Analysts at ABI Research estimate that machine learning in cybersecurity will increase spending on big data, artificial intelligence (AI) and analytics to $ 96 billion by 2021, and some of the world’s leading tech giants are taking a step to better protect their customers.
So what are the best use cases of machine learning in Cybersecurity?
1-Using machine learning to detect malicious activity and stop attacks
Machine learning algorithms help businesses detect malicious activity faster and stop attacks before they even start.
2-Using machine learning to analyze mobile networks
Machine learning is already becoming mainstream on mobile devices, but so far most of these activities have been for the use of advanced voice-based experiences such as Google Origin, Apple’s Siri and Amazon’s Alexa. However, there is still a point of reference for security. As stated above, while Google uses machine learning to analyze threats against mobile networks, a growing number of corporate firms are seeing the need to protect their own mobile devices.
3.Using machine learning to improve human analysis
At the heart of machine learning in reliability is the belief that it helps human analysts with all aspects of the business, including malicious attack detection, network analysis, endpoint protection, and vulnerability assessment.
For example, in 2016, MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) developed a system called AI 2, an adaptive machine learning safety platform that helps analysts find needles in a haystack. The system reviewed millions of logins every day, filtering the data and passing it on to analysts. Thus, the security warnings were reduced to about 100 per day.
4.Using machine learning to automate repetitive security tasks
The real benefit of machine learning is that it can automate repetitive tasks, keeping staff focused on more important tasks.
5-Using machine learning to close ZERO DAY vulnerabilities
Some feel that machine learning can help other people targeting close vulnerabilities, especially ZERO DAY threats and largely unsafe IoT devices. There is proactive work in this area: A team from Arizona State University used machine learning to track traffic on the DARK WEB to identify data on ZERO DAY exploits, according to Forbes.
