Machine learning (ML) is the new trend and buzzword in certain areas, including the cybersecurity field. It is a way for computers to respond to threats without being programmed. Machine learning is becoming more prominent, but so are emerging new threats, such as WannaCry and other ransomware. So how can machine learning be applied to cybersecurity in order to strengthen the defenses and address the emerging new threats?
All companies are vulnerable to cyberattacks these days. In order to better safeguarding their data, they are turning to machine learning, especially in the banking and technology market sectors. It was estimated by ABI research that artificial intelligence (AI), and machine learning in particular, will reach spending up to $96 billion by 2021. This includes spending on big data, intelligence, and analytics. Cybersecurity as a whole is forecasted to be worth $231.94 billion by 2022.
How does Netflix know which movies are you interested in? Based on a pattern of previously watched movies, it can propose other movies you are interested in. This is a good example of artificial intelligence being used on a daily basis. Machine learning falls under a broader umbrella of AI. To put it simple, ML refers to the concept of unsupervised learning without human choreographing it. ML is able to find patters in large databases, so it can create algorithms and learn for themselves. For the algorithm to work, it needs a lot of input, such as in form of huge datasets, called big data.
Applications of Machine Learning: Processing Big Data
This is the arena where the machine learning comes to play. Processing big data and determine what is normal and what is malicious activity is one of the great benefits of ML. With so much input, cybersecurity experts would be overwhelmed and it could take them too long to recognize cyber threats and breaches and to respond to them when the system was already infiltrated.
Detecting Advanced Breaches
For all companies, early detection and identifying security issues before the breach occurs are crucial. According to Mike Paquette, VP of Products at Prelert, machine learning is essential to “detect today’s advanced cybersecurity threats early”. Because ML focuses not only on known threats, but is also able to identify new potential threats before they were able to cause any real damage.
Because of its unique and quick adaptability to new and emerging threats, machine learning is an ideal tool for the cybersecurity field. ML is able to identify security threats, patterns and malicious activity and to compare them against data from databases to identify new threats. The Netflix analogy also applies to cybersecurity. In this case, ML can compare previous types of responses to an incident and learn what is the best response, using algorithms. Overall, it can recommend the best course of action and tasks in incident response and risk management solutions.
In the end, while machine learning will continue to boost, unfortunately, it will not solve all the problems in cybersecurity. Because also hackers can also use machine learning for developing new methods and carrying out their attacks. But ML can definitely mitigate some threats.