When it comes to cybersecurity, organizations are constantly looking for new ways to strengthen their defenses. A promising research area is combining cybersecurity and machine learning (ML). In this way, organizations can create algorithms that automatically detect potential threats and take steps to mitigate them.
In a world of exponential data growth, it’s also becoming harder to spot security threats. Cybersecurity teams and organizations use ML to help spot patterns and discrepancies in datasets that might otherwise go unnoticed.
How ML enhances cybersecurity
Organizations that have already adopted this approach have seen great results. By implementing ML, you can detect network intrusions, find anomalies, and stop them before damage is done.
For example, companies typically have logs of logins or login attempts. These logs can be transformed into datasets for training ML models. You can monitor your users’ login habits (i.e. where they connect, on what device, when, etc.) and train machine learning algorithms to recognize these patterns and flag login attempts that deviate from the pattern. . Anomalies of this sort can be a sign that someone is attempting unauthorized access.
This is just one example of how combining cybersecurity and machine learning can be beneficial. As more organizations adopt this approach, security threat detection and prevention will become more efficient.
Additionally, machine learning can be used to automatically detect new threats that are undetectable by current security protocols. As machine learning in cybersecurity continues to grow, it is expected to enable more effective and sophisticated defenses against the ever-evolving cybersecurity threat landscape.
Current and future cybersecurity
Cyberattacks are becoming more common as more companies embrace digital transformation. According to IBM research, the average cost of a data breach in 2022 will hit a record high of US$4.35 million. In just two years, average costs have risen 12.7% from his US$3.86 million in 2020.
Additionally, 83% of the companies surveyed in this study experienced multiple data breaches in 2022. Of those, only 17% said this was their first attack. Also, 60% of surveyed companies said they increased the price of their products because of the cost of a data breach.
Malicious attacks often use similar strategies. I need to trick a human user into performing a specific action. To achieve this, it must resemble a real business his communication as closely as possible in order to convince the user to take action. Otherwise, tech-savvy people and companies will ignore it or perceive it as a malicious attempt.
In fact, many new malware variants are simple mutations of the same code. We’ve worked with malicious code for decades, so we have a wealth of information that can be used as a proper machine learning training set.
As attackers mount more complex cyberattacks against enterprises, AI and ML can help protect critical infrastructure from these sophisticated attacks. In fact, these technologies are becoming more and more common for cybersecurity professionals in their constant battle against the bad guys.
Domain Generation Algorithm (DGA) is a common threat
Domain Generation Algorithm (DGA) is a technique used by cyber attackers to create millions of domain names and IP addresses. This makes it virtually impossible to discover the cause of a threat once it’s executed.
Simply put, juggling and controlling one ball is relatively easy, but doing it with hundreds or thousands of balls becomes impossible. The same is true for DGA management.
Therefore, one of the most important advantages of a DGA attack is that the perpetrator can flood the DNS with thousands of randomly formed names. Only one of those thousands is a true command and control (C&C) center, posing significant problems for experts trying to identify the source. Additionally, since DGAs are typically seed-based, attackers may pre-plan which domains to register.
Once cyber attackers release malware, it needs to be monitored and directed. C&C servers provide commands to malware-infected computers to perform actions such as denial of service (DoS), installing keyloggers, encrypting hard drives in ransomware attacks, or exfiltrating sensitive data. instruct.
DGA has been (and continues to be) a source of frustration for any cybersecurity professional. Fortunately, machine learning has already made great strides in improving detection systems. For example, Akamai has built a very complex and successful model. There are also several libraries and frameworks available for smaller market participants.
Other applications of machine learning
Apart from DGA, other attack techniques are available and can be addressed by ML in an equally efficient manner. Phishing is a great use case for machine learning. In addition to being the most common cyberattack vector, impersonation and fabrication are also used extensively to achieve their creators’ goals.
A typical phishing website (and email) tries to mimic legitimate communications exactly. Nevertheless, there are always some discrepancies, such as unexpected links, grammatical errors, text font changes, etc., and something is not always what it should be.
To avoid phishing traps, we may use cybersecurity tools and machine learning to scan individuals’ professional emails for signs of cybersecurity concerns.
You can also use natural language processing to inspect emails for unusual patterns or words that could suggest the email is a phishing attempt.
Research on phishing detection using ML suggests that training a logistic regression model over time should be able to compute phishing probabilities and assign specific websites to categories. Data collection for these models may be complex, but certain public sets are already accessible (such as his PhishTank adopted by the study authors).
Conclusion
As cyberattacks become more prevalent and more insidious in number and complexity, AI and ML can help businesses better equip themselves to thwart these threats.
With the right technology, businesses can identify and respond to cybersecurity threats in real time, while resolving potential hazards before they become serious problems. The result is reduced detection time and costs, improved corporate security posture, and the ability to keep up with the pace and scale of today’s dangers.
Machine learning can only solve some problems, such as highly specialized attacks, but it raises the bar significantly for attackers. Cybersecurity should therefore be viewed as a state-of-the-art machine learning application.
