Using Machine Learning to Identify Vulnerabilities and Prevent Cyberattacks
In today’s digital landscape, organizations are constantly seeking ways to enhance their cybersecurity defenses. One area of research showing promise is the combination of cybersecurity with machine learning (ML). By leveraging ML algorithms, organizations can automatically detect potential threats and take proactive measures to mitigate them.
As the volume of data continues to grow exponentially, discovering security threats has become increasingly challenging. To navigate this complexity, cybersecurity teams and organizations are turning to ML to identify patterns and discrepancies in datasets that may go unnoticed.
Organizations that have embraced ML in their cybersecurity efforts have experienced significant benefits. By implementing ML, they can swiftly detect network intrusions, identify anomalies, and take prompt action to prevent any damage.
For example, companies typically maintain logs of login attempts and activities. These logs can be transformed into a dataset to train ML models. These models can then monitor user login practices, such as location, device, and time, and recognize patterns. If a login attempt deviates from these patterns, it could indicate unauthorized access.
This is just one example of how combining cybersecurity with machine learning can be advantageous. As more organizations adopt this approach, its effectiveness in detecting and preventing security threats will only improve.
Furthermore, machine learning can help automatically detect new threats that existing security protocols may miss. As machine learning continues to evolve in the cybersecurity field, we can expect to see more sophisticated defenses against the ever-evolving threat landscape.
With the increasing adoption of digital transformation, cyberattacks are becoming more prevalent among firms. According to an IBM study, the average cost of a data breach reached an all-time high of USD $4.35 million in 2022. This represents a 12.7% increase from USD $3.86 million in 2020.
The study also revealed that 83% of businesses experienced multiple data breaches in 2022, with only 17% considering it their first attack. As a result of the high cost associated with data breaches, 60% of the companies surveyed indicated that they had raised their product prices.
Typically, malicious attacks employ strategies that aim to deceive human users into carrying out specific actions. To achieve this, these attacks must closely resemble legitimate business communication to convince users to take action. Otherwise, more tech-savvy individuals and companies would easily recognize and disregard them as malicious attempts.
Interestingly, many new malware variants are simply mutations of existing code. Since the cybersecurity community has dealt with malicious code for decades, there is an abundance of information available that can serve as valuable training data for machine learning.
As cyber attackers continue to employ more sophisticated techniques, AI and ML are crucial in protecting vital infrastructure against these evolving threats. These technologies are rapidly becoming commonplace for cybersecurity professionals in their ongoing battle against malicious actors.
A key challenge in cybersecurity is dealing with domain generation algorithms (DGAs). These algorithms allow cyber attackers to create a vast number of domain names and IP addresses, making it extremely difficult to trace the source of the threat.
To illustrate this, imagine juggling and controlling one ball—easy enough. Now imagine having to juggle hundreds or thousands of balls simultaneously—that becomes an impossible task. The same principle applies to managing DGAs.
One of the significant advantages of DGA attacks is the ability of perpetrators to overwhelm the Domain Name System (DNS) with thousands of randomly generated names. Only one of these thousands would be the actual command and control (C&C) center, posing significant challenges for experts trying to locate the source. Furthermore, because DGAs are typically seed-based, attackers can plan in advance which domains to register.
Once cyber attackers release malware, they must monitor it and provide instructions. Command and control (C&C) servers serve as the means of communication, issuing commands to malware-infected computers for actions like denial-of-service attacks, installing keyloggers, encrypting hard drives in ransomware attacks, or extracting essential data.
Fortunately, machine learning has already made significant progress in improving detection systems for DGAs. For example, Akamai has developed a highly complex and successful model. There are also numerous libraries and frameworks available for smaller players in the market.
In addition to DGAs, ML can effectively tackle other attack techniques, such as phishing. Phishing is the most common cyberattack vector and often relies on impersonation and fabrication to achieve the attacker’s goals.
Phishing websites and emails typically mimic legitimate communication, but there are often inconsistencies such as unexpected links, grammatical errors, or changes in text formatting. Cybersecurity tools and machine learning can be utilized to scan individuals’ professional emails for indicators of potential cybersecurity concerns.
Natural language processing can help identify unusual patterns or words that may indicate a phishing attempt. A study on phishing detection using ML suggests that with lengthy logistic regression model training, it is possible to calculate the probability of phishing and categorize websites accordingly. Although gathering data for these models can be challenging, certain public datasets are already available (e.g., PhishTank).
As the number and complexity of cyberattacks continue to grow, AI and ML can empower companies to better protect themselves against these threats. By adopting the right technologies, businesses can identify and respond to cybersecurity threats in real-time, minimizing potential damages. This leads to reduced detection time and costs, bolstering the overall security posture of the organization and enabling them to stay ahead in today’s rapidly evolving threat landscape.
While machine learning cannot solve all cybersecurity challenges, it certainly raises the bar for attackers. As a result, cybersecurity should be considered an advanced application of machine learning, constantly evolving to combat emerging threats.
