Machine Learning in Cybersecurity

There are three major types of ML models:

Supervised machine learning involves training a model on data that has already been analyzed and labeled by a human so it can learn the patterns that predict the labels and then recognize those patterns in new data. Supervised learning is good at classifying data and recognizing particular patterns specific to certain types of threats, such as distributed denial-of-service (DDoS) attacks.

Unsupervised machine learning involves training models on unlabeled data, allowing the model to find hidden patterns, structures, or groupings in the data on its own and define these clusters of characteristics. This form of ML is good at detecting new and complicated attack patterns, detecting anomalies in incoming traffic, and mitigating zero-day attacks.

Reinforcement machine learning uses trial and error to iteratively improve the model’s approach to making decisions based on rewards and penalties, continually trying new approaches to maximize the reward. This ML model is good at detecting a wide range of cyberattacks and improves its efficiency over time.

ML is increasingly applied across a wide range of cybersecurity functions due to its ability to automate complex tasks, identify patterns in massive datasets, and detect evolving threats in real time.

It contributes greatly to cyber threat intelligence by generating actionable insights from large and varied data sources, such as logs, dark web content, and threat reports, to identify emerging attack trends, threat actor behaviors, and indicators of compromise. ML models excel at anomaly detection, as they learn what constitutes normal behavior for users, devices, or applications and can detect deviations that may signal breaches, insider threats, or misconfigurations.

Similarly, machine learning can inspect network traffic flows in real time to identify suspicious patterns, such as command-and-control communications, data exfiltration attempts, or lateral movement within the network. This is crucial for detecting advanced persistent threats that evade signature-based detection.

Risk scoring is another cybersecurity tactic enabled by machine learning. ML algorithms can assess and prioritize risks by analyzing multiple data points—such as user behavior, asset sensitivity, and threat likelihood—to produce dynamic, context-aware risk scores based on the threat’s potential impact on an organization. This is especially important when detecting bots and malicious automation, as attackers will attempt to spoof telemetry signals to disguise their campaigns as legitimate traffic. For instance, they might rotate IP addresses, use different autonomous system numbers (ASNs), which are identifiers for Internet networks, or change browser user-agent strings to avoid detection. ML helps to spot these deceptive tactics by identifying subtle, unusual patterns across multiple data points that humans or rule-based systems might miss.

Machine learning is also highly valuable for malware detection, as it offers dynamic capabilities that go beyond static, signature-based security methods. ML enables faster, more adaptive, and more accurate identification of both known and unknown threats, including previously undetected malware, including zero-day and polymorphic variants.

Machine learning is increasingly used to automate elements of penetration testing, where security professionals conduct simulated cyberattacks to identify vulnerabilities in a computing system or platform. ML can reveal exploitable paths, explore vulnerabilities, or simulate attacker behavior with the goal of uncovering weaknesses before real attackers can exploit them.

These ML use cases fall into three broad categories:

• Detecting threats faster and with greater accuracy.  Security solutions that leverage aachine learning enhance the speed and precision of threat detection by analyzing large volumes of data in real time to spot anomalies, suspicious patterns, or known indicators of compromise. ML algorithms learn what constitutes normal user or system behavior and flag deviations that may indicate malicious activity so it can be addressed in real time.
• Proactively finding and patching network vulnerabilities. Identifying weak points before they are exploited by attackers is key to proactive threat management. ML-supported security tools can use threat intelligence, simulation, and modeling to score and prioritize vulnerabilities for rapid remediation.
• Improving IT efficiency by automating repetitive tasks. While many cybersecurity operations are repetitive and time-consuming, ML can help automate these tasks, increasing the speed and consistency of security operations and allowing security teams to focus on strategic planning and decision-making.

The primary benefit of incorporating ML models into cybersecurity is the ability to analyze massive quantities of diverse data—such as network traffic, user behavior, system logs, and threat intelligence—at speed and scale far beyond human capacity. Machine learning excels at identifying complex patterns, correlations, and anomalies in real time, allowing security systems to detect and respond to threats earlier in the attack lifecycle, often before significant damage occurs. Additionally, ML algorithms continuously improve as they ingest more data, making detection smarter and more adaptive over time.

For businesses, this means stronger security postures, as ML-enabled solutions can help automate security policy updates, expose existing weak points, proactively remediate vulnerabilities, and reduce the possibility of human error introducing a weakness or misconfiguration. Secuity solutions that use ML also allows businesses to adapt faster to the evolving cyberthreat environment by detecting new threats quickly and adjusting defense systems proactively. Improved IT productivity is another benefit of incorporating ML into cybersecurity systems. By automating most threat detection and mitigation efforts, these systems free up human IT resources to work strategically and augment their tactics with real-time insights and threat intelligence.

To gain the full impact and benefits that ML can bring to cybersecurity requires access to high-quality data and telemetry, as these are the fuel that drives accurate, adaptive, and effective models. Without access to automated sources of data, ML systems cannot continue to learn, improve, or provide meaningful insights. For many organizations, providing these automated data flows is a challenge.