Using Machine Learning to Find Vulnerabilities and Prevent Cyberattacks

machine learning cyberattacks
machine learning cyberattacks

When it comes to cybersecurity, organizations are constantly looking for new ways to improve their defenses. A promising area of research is combining cybersecurity with machine learning (ML). This way, organizations can create algorithms that automatically detect potential threats and take steps to mitigate them.

In a world where the volume of data is increasing exponentially, the difficulty of discovering security threats is also escalating. Cybersecurity teams and organizations are turning to ML to help them find patterns and discrepancies in datasets that might otherwise go unnoticed.

How ML Empowers Cybersecurity

Organizations that have already adopted this approach have seen great results. By implementing ML, they can detect a network intrusion, find the anomaly and stop it before any damage is caused. 

For example, a company usually has logs of login or login attempts. Those logs can then be turned to a dataset to train a ML model. It can monitor user login practices (i.e., their connection location, with what device, at what times, etc.), and a machine learning algorithm can be trained to recognize those patterns and flag any login attempts that deviate from them. An anomaly of this kind could be a sign of someone trying to gain unauthorized access.

This is just one example of how combining cybersecurity with machine learning can be beneficial. As more and more organizations adopt this approach, it will become even more efficient at detecting and preventing security threats. 

Additionally, machine learning can be used to automatically detect new threats that current security protocols cannot detect. As machine learning in cybersecurity continues to grow, we expect to see more effective and sophisticated defenses against the ever-evolving cybersecurity threat landscape. 

Current and Future Cybersecurity 

Cyberattacks are becoming increasingly common as more firms embrace digital transformation. According to an IBM study, in 2022, the average cost of a data breach reached an all-time high of USD $4.35 million. In just two years, the average cost has risen by 12.7% from USD $3.86 million in 2020.

In addition, 83% of businesses included in this study had more than one data breach in 2022. Of those, only 17% indicated this was the first attack they experienced. And due to the cost of data breaches, 60% of the polled companies said they raised the price of their products.