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Combine Machine Learning and UEBA for Advanced Threat Detection

Threat detection is a critical security measure for organizations today. Cybercrime has become increasingly common and costly, so reactive measures are insufficient. You must hunt for threats proactively. Tools like machine learning (ML) and user and entity behavior analytics (UEBA) make that process easier. 

Combining UEBA and machine learning can provide the responsiveness and accuracy organizations need for advanced threat detection. Here’s a closer look. 

What Is UEBA? 

User and entity behavior analytics build on the concept of user behavior analytics (UBA). UBA refers to analyzing how people act on networks to detect unusual behavior, indicating a potential breach. UEBA extends this analysis to include servers, routers and endpoints, not just users. 

The need for UEBA over UBA stems mainly from rapid Internet of Things (IoT) adoption. Enterprises now manage roughly 135,000 endpoints on average and IoT devices, with their minimal built-in protections, represent a considerable portion of this figure.  

These massive attack surfaces mean cyber criminals can use nonuser entities to move laterally through a network, not just breached accounts. Consequently, your threat detection must account for devices and users, making UEBA a more thorough practice than UBA. 

How Machine Learning and UEBA Work Together 

Many UEBA solutions leverage machine learning to improve this process. Rule-based approaches to behavior analytics quickly become impractical when you must define rules for hundreds of thousands of entities. ML can automate the process and adapt to changing patterns. 

Machine learning-powered UEBA automatically categorizes user behavior and standard endpoint activity to establish baselines for normal activity. These insights combine with analysis of past security incidents to detect anomalies faster and more accurately. 

Unlike rules-based threat detection, ML-based UEBA can account for changes in a person’s or entity’s role or situation. Similarly, it can score risks on a scale instead of taking a black-and-white approach to anomaly detection, bringing more nuance into the process. 

How ML and UEBA Improve Threat Detection 

Cybercrime has grown 600% since the COVID-19 pandemic, and organizations must ensure they have reliable advanced threat detection processes. ML-powered UEBA is one of the best tools for the job. Here’s how these technologies offer the threat detection improvements modern businesses need. 

Faster Incident Response 

One of UEBA’s most significant advantages is its speed. Machine learning models can detect anomalies throughout a network far faster than a manual process could, especially in an organization with thousands of entities to manage. 

Recent security incident research emphasizes how substantial a difference machine learning makes. Organizations with fully deployed AI security tools identify and contain breaches 28 days faster on average than those without them. Those faster response times translate into $3.05 million in savings and, in some cases, may prevent hacks entirely. 

Incident detection that fast is virtually impossible with manual methods. You’d need a dedicated team to monitor every device 24/7, making ongoing cybersecurity talent shortages highly impractical. ML-powered UEBA lets you remain constantly vigilant despite these workforce challenges. 

Detecting Insider Threats 

Machine learning and UEBA are more effective at spotting insider threats than alternative solutions. Rules-based network monitoring may overlook unusual activity from an insider account if it’s acting within its normal location and time. Because ML-enabled UEBA can account for a wider range of activity and establishes more nuanced, accurate baselines, it can spot potential privilege misuse. 

UEBA with machine learning can apply role- and situation-based access privileges to identity and access management. Consequently, it can spot and stop suspicious insider activity more effectively. 

Insider threats have risen by 44% over the past two years, with their average costs experiencing a similar increase, reaching $15.38 million. Response times to these incidents have also slowed, taking 85 days on average, so businesses need more reliable controls like ML-driven UEBA. 

Fewer Errors 

Automating threat detection through ML and UEBA also reduces the risk of errors. Simpler, more manually involved tools are prone to mistakes, especially false positives. One study found that 45% of web app security alerts are false positives, and addressing these results in the same amount of downtime as an actual attack. 

Machine learning models continually adapt as they gather more data to better understand different situations and how access privileges change. This nuance makes them less likely to flag innocent behavior as a potential breach. Avoiding these mistakes gives you more time and resources to address real threats. 

ML-Powered UEBA is a Crucial Security Tool 

Threat detection must evolve as networks become increasingly complex and cybercrime rises. ML-enabled UEBA is a critical part of the solution. 

Machine learning behavior analytics tools offer the accuracy, speed, reliability and coverage you need to effectively monitor your network against all threats. In the wake of rising cybercrime incidents and costs, that’s quickly becoming a necessity, not just a helpful upgrade. 

Zac Amos
Zac Amos
Zac Amos is the Features Editor at ReHack, where he covers trending tech news in cybersecurity and artificial intelligence. For more of his work, follow him on Twitter or LinkedIn.

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