Using Machine Learning to Find Vulnerabilities and Prevent Cyberattacks – Source: securityboulevard.com

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. 

Often, malicious attacks use a similar strategy. They must deceive a human user into carrying out particular actions. To achieve this, they must resemble authentic business communication as much as possible to convince users to take action. Otherwise, more tech-savvy people and companies will disregard it or recognize it as a malicious attempt.

Actually, many new malware variants are simple mutations of the same code. Since we have been dealing with malicious code for several decades, there is plenty of information available that can be used as decent machine learning training sets. 

As attackers conduct more complex cyberattacks on businesses, AI and ML can help protect vital infrastructure against these sophisticated attacks. Indeed, these technologies are becoming increasingly commonplace for cybersecurity professionals in their continuous war against bad actors.

Domain Generation Algorithms (DGAs) are a Common Threat

A domain generation algorithm (DGA) is a method that cyberattackers use to create a huge number of domain names and IP addresses. It makes it practically impossible to discover the source of the threat when carried out. 

In simple terms, juggling and controlling one ball is relatively easy, but it would become an impossible task if you had to do it with hundreds or thousands of balls. The same goes for managing DGAs. 

That is why one of the most significant advantages of DGA assaults is the perpetrator’s ability to flood DNS with thousands of randomly formed names. Only one of those thousands would be the true command and control (C&C) center, posing considerable problems for any expert attempting to locate the source. Furthermore, because DGAs are typically seed-based, the attacker may plan which domain to register for in advance.

Once cyberattackers release malware, they must both monitor it and feed it instructions. C&C servers provide commands to malware-infected computers, instructing them to accomplish actions like denial-of-service (DoS), installation of keyloggers, encrypting hard drives in a ransomware attack or the extraction of essential data.

DGAs were (and continue to be) a source of frustration for any cybersecurity practitioner. Fortunately, machine learning has already enabled us to make significant progress in improving detection systems–Akamai, for example, has built a highly complex and successful model. And there are several libraries and frameworks available for minor market participants.

Other Applications of Machine Learning

Apart from DGAs, other attack techniques can be used and, in the same efficient way, tackled by ML. Phishing is an excellent use case for machine learning. Aside from being the most common cyberattack vector, it also extensively uses impersonation and fabrication to achieve its creators’ goals.

A typical phishing website (and email) attempts to exactly mimic legitimate communication. Nonetheless, there will always be some inconsistency, such as an unexpected link, a grammatical fault or a text font change – something is always not the way it should be. 

To avoid phishing traps, cybersecurity tools and machine learning may be used to scan individuals’ professional emails to see if any indicators signal a cybersecurity concern.

Natural language processing may also be used to examine the emails for any unusual patterns or words suggesting that the email is a phishing endeavor.

According to a study on phishing detection using ML, lengthy logistic regression model training should be able to calculate a phishing probability and allocate a given website to a category. Though gathering data for these models may be complicated, certain public sets are already accessible (e.g., PhishTank, adopted by the study’s authors). 

Conclusion

As the number and complexity of cyberattacks become more prevalent and more cunning, AI and ML can assist companies in becoming better equipped to thwart these threats. 

With the correct technologies, businesses can identify and react to cybersecurity threats in real-time while also resolving potential dangers before they become significant problems. Consequently, detection time and costs are reduced and the company’s security posture improves, allowing businesses to keep up with the pace and magnitude of today’s hazards.

Although machine learning can only solve some issues such as highly specialized attacks, it will significantly raise the bar that attackers must clear. As a result, cybersecurity should be regarded as a cutting-edge machine learning application.