Machine Learning in Cybersecurity: 5 Real-Life Examples

From real-time cybercrime mapping to penetration testing, machine learning has become a crucial part of cybersecurity. Fortunately, machine learning can help solve the most common tasks, including pattern detection, prediction, regression, and classification.

In an era of large amounts of data and a shortage of network security talents, machine learning seems to be an alternative to solve many problems. Indeed, through machine learning, when applied to computer security, we can sort through millions of files to discover threats. Microsoft Windows Defender, for example, employs multiple layers of machine learning to block potential threats. 

Here are a few ways cybersecurity uses machine learning.

1. Signature-based Threat Detection

In computer security, old-fashioned threat detection systems use heuristic and static signatures to detect threats and anomalies. For example, anti-virus software will generate and maintain a virus signature database according to the characteristics of the virus program. It will then identify the virus program by comparing it with the signature in the database when checking and removing it. 

Although the signature-based threat detection technology is easy to understand, it is not robust. One of its biggest problems is how to ensure that the signature comparison process matches the data inflow speed when the data size and data flow rate increase significantly. Each packet needs to be compared with each signature in the database.

If the synchronization cannot be maintained, only part of the data packet can be discarded. Consequently, it results in a “fish through the net”. Today, signature-based systems are gradually being replaced by intelligent cybersecurity agents. ML has made positive progress in this regard by identifying new types of malware, zero-day attacks, and advanced persistent threats (APT). 

2. Advanced Persistent Treats

Generally speaking, it is difficult to prevent an attack entirely due to its complex nature. ML can identify the attack in the early stage of the attack and prevent it from spreading to the entire system. Many network security companies practice advanced analysis methods applying machine learning to recognize APT attacks in the early stages of the threat life cycle. This method can effectively prevent identity data leakage and internal threats. Prescriptive analytics is more responsive. indeed, It analyzes what response measures to take after a cyber-attack occurs to minimize losses. 

3. Performance Tuning and Error Detection 

Performance tuning and error detection are the most important iterative processes of a machine learning system, which can help improve the performance of the system. We can say that the system is performant if the generalization function of the system can give a lower generalization error with a higher probability. More and more companies are adopting machine learning to help them work safely in modern IT environments. 

4. Vulnerability Scans

Machine learning is also applied to scan networks for vulnerabilities and automate processes. Almost all cybersecurity companies employ machine learning in their products for threat detection and protection. In the field of network security, the role of ML in network security is to identify the behavior patterns of users, data, equipment, systems, and networks, and to distinguish abnormal from normal. It also helps administrators to analyze large amounts of data, investigate new types of threats, and respond to threats faster. 

5. Corporate Security

From insider threats to abuse of privileges and management to hackers, humans are important and diverse carriers of cyber risks. Therefore, Machine Learning help detect changes in the way users interact in the IT environment and describe their behavioral characteristics in the attack environment. Despite high marketing requirements, the reality is that the corporate security environment is a huge and dynamic network.

And managers must constantly monitor, audit, and update based on continuous, unpredictable, internal, and external threat vectors. ML introduces various enhancements in the ability to detect, investigate, and respond to threats. But it is a combination of personnel and technology that can manage a full range of threats in the ever-evolving security environment.

These were few examples of how ML relates to computer security. More and more security companies are implementing ML in their processes, especially applied to early treatment detection. In the future, we expect to see more applications of ML within the industry. If I missed something, please share other use cases as to how ML is applied to Cybersecurity. You can find some references and external readings at the bottom of the article.

References

• 5 Top Machine Learning Use Cases for Security. (n.d.). Retrieved from mdsny.com
• Author, G. (2020, August 18). What is Machine Learning and How Does It Benefit Cybersecurity? Retrieved from Security Boulevard: https://securityboulevard.com/2020/08/what-is-machine-learning-and-how-does-it-benefit-cybersecurity/
• Gottsegen, G. (2020, January 08). MACHINE LEARNING CYBERSECURITY: HOW IT WORKS AND COMPANIES TO KNOW. Retrieved from Builtin.com:
• Polyakov, A. (2018, Oct 04). Machine Learning for Cybersecurity 101. Retrieved from towardsdatascience: 

Article refactored from How machine learning is used in Cybersecurity? [in 2021]