How is Machine Learning Used in Cyber Security?

Machine Learning Used in Cyber Security 2021
Machine Learning Used in Cyber Security 2021


Fortunately, machine learning can help to solve tasks, including regression, forecasting, and classification. Machine learning has many purposes like identifying cyber threats, improving available antivirus software, fighting cybercrime that also uses artificial intelligence capabilities, etc. In the era of large amounts of data and a lack of cybersecurity talent, ML seems to be the only solution. A subset of artificial intelligence, machine learning uses algorithms from previous datasets and statistical analysis to make assumptions about a computer’s behaviour. The computer can then regulate its actions and even perform functions. With its ability to classify millions of files and identify potentially dangerous ones, machine learning is increasingly used to discover threats and automatically remove them before they can wreak havoc.

In addition to early threat identification, machine learning searches for vulnerabilities in the network and automate responses. Machine learning does some things very well, such as quickly scanning large amounts of data and analyzing them using statistics. Cybersecurity systems generate a large amount of data, so it’s no surprise that technology is such a useful tool. It also has some applications like identifying cyber threats, improving available antivirus software, fighting cybercrime, etc.

Today, it is impossible to implement effective cybersecurity technology without relying heavily on machine learning. It is impossible to effectively implement machine learning without a comprehensive, rich and comprehensive approach to the underlying data. With machine learning, cybersecurity systems examine the patterns and learn from them to avoid similar attacks and react to developing behaviours. It helps cybersecurity teams be more practical in preventing threats and responding to dynamic attacks in real-time. It reduces the amount of time spent on routine tasks and allows organizations to use their resources more strategically. But you can only do these things if the underlying data that supports machine learning provides a complete picture of the environment. And with the increasing recognition of artificial intelligence and machine learning, these technologies are becoming major players in the cybersecurity field.

Cybersecurity engineers detect, investigate and prevent attacks. They solve problems with technology software and IT equipment. Specifically, security engineers install firewalls, implement breach detection systems, and collaborate with other professionals to troubleshoot security issues. They report the findings and provide recommendations to company executives. One of the best ways to demonstrate network security proficiency is to get some network security engineer certifications. Several organizations offer network certifications that focus on knowledge or an overview of the network experience. For becoming a cybersecurity engineer, there are few essential certifications like Certified Ethical Hacker (CEH), Certified Information Systems Security Professional (CISSP) certification course for any network administrator or engineer focused on securing and managing corporate networks.

Many stages in the threat intelligence process get improved through machine learning. In the real world, most companies use 2-3 solutions to meet their threat intelligence requirements. Therefore, machine learning-based cybersecurity software is fast becoming a necessity and not just a luxury.

This article is an introduction to provide a practical technical understanding of current advances & future directions of ML research applied to cybersecurity.


Regression (or prediction) is simple. Knowledge of existing data helps in getting an idea of ​​the new data. Let’s take an example of forecasting the price of a house. In cybersecurity, it helps in fraud detection. Characteristics (for example, the total number of suspicious transactions, location, etc.) determine the likelihood of fraudulent actions.

All methods get divided into two broad categories: machine learning and deep learning.


Sorting is also simple. Suppose you have two stacks of images classified by type (for example, cars and bikes). In conditions of cybersecurity, a spam filter that separates spam from other messages can serve as an example. Spam filters are probably the first approach to cybersecurity activities.

The supervised learning approach is generally used for classification when examples are from certain groups. 


It is similar to classification. Information on data classes is unknown. There is no initiative whether this information can be classified. It is unsupervised learning.

Presumably, the best task for the pool is forensic analysis. The reasons, course and consequences of an accident are unclear. All activities must be classified to find anomalies. Malware analysis solutions (such as malware protection or secure email gateways) execute it to separate legal files from outliers. Another area where clustering can be applied is the analysis of user behaviour. In this case, the application users get grouped so that you can see if they belong to a particular group. 

Clustering is typically not applied to solve a particular cybersecurity task.


Dimensionality reduction

The reduction or generalization of dimensionality is not as popular as classification, but it is necessary for working with complex systems with unlabeled data and many potential features. Clustering cannot be applied because typical methods limit the number of functions or do not work. Reducing dimensionality can help manage it and eliminate unnecessary functionality. Like clustering, reducing dimensionality is often one of the tasks of a more complex model. When it comes to cybersecurity tasks, scaling down is crucial for face detection solutions – the ones you use on your iPhone.

Generative models

The task of generative models is different from those mentioned above. While these activities deal with existing information and associated decisions, generative models simulate actual data based on previous findings. The simple task of offensive cybersecurity is to create a list of input parameters to check a particular application for injection vulnerabilities. Instead, you can have a susceptibility scanning tool for web applications. One of its modules is testing records for unofficial access. These tests can change the names of existing files to identify new ones.


Machine learning remains a relatively new addition to the cybersecurity field. The only thing to keep in mind is that machine learning algorithms must minimize their false positives – actions that they identify as malicious or that are part of a cyber attack but are not. Businesses should consult with their cybersecurity specialists, who can provide the best solutions to identify and manage new and different types of cyberattacks even more accurately using machine learning.




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