August 31, 2020

What’s the Role of ML in Cybersecurity?

Today the world is turning into a connected community, where people are familiar with digital technologies from childhood and hardly remember the times when iPhones did not exist. However, in the pursuit of advanced and convenient technologies, few people think about the security of connected networks. Advances in artificial intelligence have ushered in a whole new era where virtually any data collected is processed and analyzed using machine learning algorithms. As the number and complexity of cyberattacks grows, artificial intelligence (AI) helps security analysts stay ahead of threats even when resources are scarce.

With high-quality machine learning models, you can get rid of manual work and focus on strategically important tasks, while machine learning services that match your requirements will allow you to gain valuable insights into the company’s performance.

Machine learning models are trained to “understand” the threats and risks to cybersecurity, repel the attack, and identify patterns by placing learning nodes in all areas of protection. The use of machine learning to increase cybersecurity opens the possibility of creating an automated system for finding and detecting cyberattacks. 

By implementing machine learning, organizations can develop a customized AI-powered action plan to improve threat detection and response, gather information, and think about the relationships between threats, and predict the actions of the attacker. Having trained on a large number of samples, the ML-model is able to generalize information and detect future threats. AI eliminates time-consuming research and provides out-of-the-box risk analysis, reducing the time analysts need to make key decisions and coherently address threats.

Due to its success in solving clustering and classification problems, machine learning does an excellent job of identifying anomalies. In particular, machine learning is used in the following cases:

  • recognition of fake documents, biometric data, and other identifiers;
  • detection of fraudulent transactions (antifraud), for example, when the scenario of using a bank card differs from the usual one;
  • detection of leaks due to the illegal actions of privileged users, for example, administrators who steal or delete important data. Machine learning algorithms will allow you to correlate several features (amount and type of data, time, protocol, recipient address) in order to separate the planned unloading of a new version of the database or distributions for remote offices from information theft.

Let’s look at the solutions that successfully apply artificial intelligence and machine learning to improve cybersecurity.

User and Entity Behavior Analytics 

User and Entity Behavior Analytics (UEBA) implies an analysis of user behavior and other entities. This analysis has been greatly improved in recent years. User profiling and anomaly detection are now difficult to imagine without statistical and cluster analysis. Through the use of machine learning models, UEBA has greatly departed from the principles of SIEM solutions and now relies not on signature rules, but on the results of working out the corresponding models, which allows you to predictively respond to potential threats associated with insider activities and identify compromised accounts before the occurrence of serious incidents.

Thanks to well-tuned clustering and statistical analysis algorithms, modern UEBA solutions accurately form user and asset behavior profiles (behavioral baseline). After that, classification and regression analysis algorithms come into operation, which look for anomalies in the built profiles of behavior, on the basis of which the classical rules of correlation signal possible incidents.

Next-Generation Firewall

The advent of machine learning tools has significantly improved the corresponding solutions. Now that you can fully profile traffic and detect anomalies in it, detecting DDoS attacks has become a more trivial task. Predictive construction of a profile and an attack vector allows dynamic multi-stage filtering of incoming traffic. It is also worth mentioning the detection of DGA domains, implemented by solving classification problems. The algorithms used, trained on a labeled sample, make it possible to identify malicious DGA domains in real-time based on the principle of N-gram analysis.

Information Security Tools based on Computer Vision

Modern information security systems from NSD are ready in real-time to recognize objects through a webcam and record facts of violation of security policies, for example, detect an illegitimate person, a smartphone photographing a screen, an IP camera, etc. These capabilities are especially important today when many organizations have moved their employees to work remotely, but do not want to lose control over them. 

With the help of lightweight agents, centrally installed on the workstations of employees, you can exercise full control over their activities and compliance with corporate regulations outside the office. A similar scenario is relevant for the banking and government sectors, where employees work with critical and often classified information.

Technological advances make it possible to create security systems that are constantly learning, evolving, adapting, and looking for new ways to prevent previously unknown types of attacks. Companies should work to replace reactive defenses with interactive ones that continually track the latest cybercriminal techniques. Humanity is on the cusp of a new era in which artificial intelligence and machine learning will force it to redefine approaches to cybersecurity.

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