In the messy world of security, you can forget about the days of obvious attacks that even the most basic defence systems could spot a mile off. These days, the attackers have gotten a lot more subtle; they're running low-key operations, using AI-driven tactics that fly right under the noses of anyone not paying close attention. No wonder then that some pretty alarming numbers keep turning up: the IBM report for last year found that the average company doesn't even figure out it's been breached till 194 days in, that's 6 months. And that's not the only bit of bad news: a whacking 88% of IT people will admit straight out that they just don't have the right behavioural analytics in place to even get a sniff of what trouble's brewing. breached 88% It was only a matter of time before security teams started thrashing about trying to get this whole behavioural analytics thing sorted out with machine learning at the helm. The idea is pretty straightforward: these systems just keep soaking up what normal behaviour looks like - user habits, device interactions, workload patterns - the whole shebang & by doing that they can pick up on those tiny little things that just don't quite add up - a login at some odd hour of the night, an unexpected file transfer or some other weird discrepancy. What Is Behavioral Analytics in Cybersecurity? Behavioral analytics combines big data and AI/ML to establish baselines of normal user and system activity, then detect anomalies. As one security specialist notes, behavioral analytics in cybersecurity “uses machine learning (ML) and artificial intelligence (AI) to analyze patterns in user and entity behavior within networks, applications, and other digital environments,” flagging any deviations that may signal a threat. one security In other words, instead of just matching known signatures, the system watches how users behave. For example, if an employee who normally logs in from New York at 9 AM suddenly connects from an unfamiliar country at midnight, the deviation is flagged for investigation. users Unlike traditional SIEMs or firewalls, behavioral analytics (often called UEBA, User and Entity Behavior Analytics) looks for statistical oddities. As CrowdStrike explains, it “focuses on user behavior within networks and applications, watching for unusual activity that may signify a security threat”. UEBA extends this concept beyond people to devices, servers, cloud resources, and even IoT gear. CrowdStrike Collectively, these systems can discern patterns of normality so well that they "identify trends, anomalies, and patterns" in usage data. In practice, behavioral analytics tools ingest logs, network telemetry, and activity records to build a dynamic model of normal activity. Deviations, even if they don’t match any known malware signature, trigger alerts. Machine Learning for Anomaly Detection Machine learning is the engine behind modern behavioral analytics. Once raw activity data is collected, ML models learn the regular rhythms of the network and its users. Unsupervised or semi-supervised algorithms are often used because labeled examples of every possible attack simply don’t exist. These models create a behavioral baseline and then continuously compute how far new events stray from that baseline. As Zscaler’s threat lab notes, behavioral analytics “leverages machine learning to identify anomalies and highlight potential threats before they escalate”. In essence, the ML model assigns a risk score to each action based on how abnormal it is, and actions with high anomaly scores are escalated to analysts. Zscaler’s Common approaches include clustering and statistical models (e.g., k-means clustering, Gaussian models), tree-based methods (like Isolation Forests), and even neural networks (autoencoders) that learn to reconstruct normal behavior. For instance, an autoencoder neural net might learn to compress and decompress network traffic patterns; any data point it can’t reconstruct well is treated as anomalous. In practice, products use a mix of techniques. CrowdStrike’s Falcon platform, for example, “leverages ML models that are trained on trillions of daily security events, establishing baselines for normal behavior to effectively detect deviations,” and it “employs AI-powered insights to monitor user and entity behavior, identifying suspicious activity patterns in real time”. CrowdStrike’s Splunk’s Enterprise Security similarly “continuously learns and baselines normal user and entity behavior to detect subtle deviations that indicate insider threats and advanced attacks”. The power of ML is that it can adapt over time: as “more data flows into the system, machine learning refines its criteria, creating an evolving model that adapts to new tactics used by cybercriminals”. Splunk’s Importantly, ML-driven behavioral analytics can reduce alert fatigue. Instead of clinging to rigid rules, it scores and prioritizes alerts so analysts only see the most critical issues. As one expert observes, UEBA and AI “virtually eliminate alert fatigue so analysts can focus on important alerts,” tracking patterns across users and devices and only surfacing truly high-risk anomalies. Key Use Cases for Behavioral Analytics Behavioral analytics shines in areas where adversaries mimic legitimate users or move laterally. Some of the most common use cases include: Insider Threat Detection: You'd be surprised how well BA systems can spot a trusted user suddenly behaving out of character. They do this by keeping an eye out for any unusual patterns in things like login times and file access. For example, "Behavioural analytics is a real help in identifying suspicious activity that could be indicative of some kind of malicious activity coming from within the organisation". Insider Threat Detection: You'd be surprised how well BA systems can spot a trusted user suddenly behaving out of character. They do this by keeping an eye out for any unusual patterns in things like login times and file access. For example, "Behavioural analytics is a real help in identifying suspicious activity that could be indicative of some kind of malicious activity coming from within the organisation". Insider Threat Detection: Ransom and advanced persistent threat prevention: The early stages of a ransomware attack, or an ongoing campaign of persistence, often involve some pretty subtle anomalies (like unusual file encryption processes or atypical login scripts). BA systems can catch these early on and block or flag them before it's too late. Zscaler's research has highlighted the importance of BA in preventing ransomware attacks. Ransom and advanced persistent threat prevention: The early stages of a ransomware attack, or an ongoing campaign of persistence, often involve some pretty subtle anomalies (like unusual file encryption processes or atypical login scripts). BA systems can catch these early on and block or flag them before it's too late. Zscaler's research has highlighted the importance of BA in preventing ransomware attacks. Ransom and advanced persistent threat prevention: research Fraud and Financial Abuse: In banking and e-commerce, machine learning can identify the outliers in transaction patterns. If multiple big payments come from a new device, or some data gets exported in a way that's way outside what a user normally does, it's a big red flag. More and more platforms are now using user-behavior models to spot these fraud indicators before the money disappears. Fraud and Financial Abuse: In banking and e-commerce, machine learning can identify the outliers in transaction patterns. If multiple big payments come from a new device, or some data gets exported in a way that's way outside what a user normally does, it's a big red flag. More and more platforms are now using user-behavior models to spot these fraud indicators before the money disappears. Fraud and Financial Abuse: Compromised Account Activity: Even outside of targeted attacks, attackers can use stolen credentials to "blend in". BA systems can spot this by looking out for odd logins or privilege escalations. Splunk points out that its User and Entity Behaviour Analytics (UEBA) detects "subtle deviations in user and entity behaviour, enabling early identification" of compromised accounts. Compromised Account Activity: Even outside of targeted attacks, attackers can use stolen credentials to "blend in". BA systems can spot this by looking out for odd logins or privilege escalations. Splunk points out that its User and Entity Behaviour Analytics (UEBA) detects "subtle deviations in user and entity behaviour, enabling early identification" of compromised accounts. Compromised Account Activity: Splunk Network and Endpoint Anomalies: If there's some weird network traffic (like data exfiltration patterns), access from an unfamiliar location, or some rogue application on an endpoint, BA systems can detect it. By comparing what's going on against what's normal, the system can catch threats that traditional tools might miss. Network and Endpoint Anomalies: If there's some weird network traffic (like data exfiltration patterns), access from an unfamiliar location, or some rogue application on an endpoint, BA systems can detect it. By comparing what's going on against what's normal, the system can catch threats that traditional tools might miss. Network and Endpoint Anomalies: In practice, these tools augment threat hunting and SOC operations. Automated alerts from behavioral analytics give analysts high-fidelity leads: rather than scanning thousands of rule-based alerts, they investigate “only when there’s a true risk,” cutting through noise. alerts Real-World Solutions and Case Studies Several top cybersecurity vendors are now using behavioral analytics and machine learning to stay one step ahead of the bad guys: CrowdStrike Falcon CrowdStrike’s AI-driven Falcon platform uses some seriously advanced machine learning to keep an eye on endpoints, identities, and cloud workloads. It takes in logs and other data from all these areas and stitches them together to form a picture of what normal looks like. Then it uses that image to spot when something isn't quite right. CrowdStrike’s As one CrowdStrike spokesperson puts it, the Falcon platform relies on machine learning models that have been trained on an absolutely massive amount of data - trillions of events per day - to flag up those anomalies. In the real world, this makes it spot things like unusual lateral moves or some guy trying to misuse a password that a traditional signature scanner would have missed. One of CrowdStrike's analysts says that adding behavioural analytics to the mix basically gives you an extra layer of protection, as it can catch things that other security measures just can't spot. Splunk Enterprise Security (ES) with UEBA Splunk's ES now comes with a built-in User and Entity Behaviour Analytics module. When Splunk talks about UEBA, it says it uses behaviour-based anomaly detection and machine learning to spot the tiny deviations in user and entity behaviour that could indicate something dodgy is going on. User For instance, if a system admin suddenly starts accessing a bunch of servers that they don't normally touch, the Splunk risk scoring system will automatically flag that up as a high-priority risk. And it's not just that - Splunk also uses automated risk scoring, using all the behavioural data it has about users, devices, and apps to build up an Entity Risk Score, so the SOC team just gets a single view of who or what looks like they're up to no good. As a result, when you use Splunk, you just see a list of the top risks, cutting out all the noise and false positives. Darktrace (Active AI) Darktrace’s "Enterprise Immune System" is built on unsupervised machine learning, so it doesn't need to be told what a threat looks like - it just observes what's going on and learns a kind of "pattern of life". Darktrace's CISO told us about one case where the system picked up on some crypto-mining malware that had spread to an access-control server in an empty office. Darktrace’s Why? Because the AI "knew what normal looked like" in that environment, it immediately spotted that the server was doing something that didn't fit in with what it expected. In other words, the Darktrace AI didn't have a rule that said "crypto-mining is bad" - it just saw that the server was doing crazy things and flagged it up. This is real proof of how machine learning based behavioural analytics can pick up on threats that no one's even seen before. Across the board, big organisations are finding that these machine-learning solutions are making a massive difference to how quickly they can spot and respond to threats. For example, a recent report said that using threat intelligence and analytics can shave weeks off the time it takes to spot an attack - people using such insights found out about intrusions about 28 days faster than the average. And let's be clear: that kind of speed can mean the difference between just patching up a small incident and having a full-blown breach on your hands. report Implementation Challenges and Best Practices While behavioural analytics is an incredibly powerful tool, the truth is, it's not a magic bullet when it comes to getting results. To actually see success, you need to have solid data, get the tuning right, and get your organisation onside. One thing that can be a real major concern is privacy. I mean, monitoring user behaviour, even if it's in the name of security, can raise all sorts of governance headaches. Security teams have to find a way to balance collecting data with being super clear about their policies and being transparent about what they're logging. And that means making sure users know exactly what is going on. privacy Another thing that can trip teams up is false positives. Even the smartest models can get it wrong sometimes, so they need to regularly go back and retrain the models - and also keep an eye on how sensitive they are. Zscaler puts it really well - getting the thresholds right and regularly refining the models is pretty much essential if you want to stop getting spammed with legitimate anomalies that are just a result of the system being a bit over-sensitive. false positives Zscaler And then of course there's the issue of integration. Behavioral analytics really comes into its own when it's used to complement existing tools, not try to replace them. The best way to do that is to get staff educated on how to interpret and act on these new types of alerts, and get BA singing with SIEM/EDR workflows. Behavioral analytics To be honest, market leaders will tell you that getting the most out of UEBA requires using it alongside network detection (NDR), endpoint detection (EDR), and getting threat intelligence feeds in there to get alerts that actually have some real context. UEBA You also need to make sure you've got enough compute and storage to handle the volume of log data you'll be running through - analysing all that stuff is a real resource hog. Starting off with high-value use cases (like, say, critical servers or super-restricted accounts) is a good way to get the system right before unleashing it on the rest of the organisation. Conclusion Behavioral Analytics is a major leap forward for proactive, intelligence-driven security - the sort of game-changer that fundamentally changes the way you have to think about security. These ML-based systems keep on and on modelling what normal behaviour looks like, only surfacing the truly weird stuff that happens, giving your SOC team an early warning system that can spot a sneaky attack before it gets too far. As one industry insider puts it, "Behavioural Analytics is the tried and trusted way to stay ahead, it's the key to navigating the ever-evolving threat landscape and finding your way - even just a little bit - to a safer digital world". As hackers get more and more clever and sophisticated, relying just on static ways of defending yourself is no longer an option. one industry Instead, the smart money is on abandoning those tired old ways in favour of using AI-powered behaviour monitoring, something that's already been shown to make life much, much better for beleaguered and incident response teams. What this means in real terms is being able to catch a cryptominer or an APT before they get a chance to start secretly siphoning off your data, not after they've made off with all your gigabytes. For those in charge of cybersecurity, the message is clear: if you don't embed ML-driven behavioural analytics into your defence arsenal, you're more or less leaving yourself open to getting caught out by threats that old-school signature-based tools just can't spot.

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

Behavioral Analytics in Cybersecurity: Tracking Anomalies with Machine Learning Models

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

The Next Palantir Could Ignite in Tel Aviv — Built on Soft-Signal Intelligence, Not Surveillance

Automation Is the Essential Part of Cyber Risk Management: Know Why

‘It’s Never Too Late To Learn a New Language’: Yair Solow, CEO of Centraleyes

The Benefits of SOC Reports for Cybersecurity

What Is Cyber Risk Quantification?

Large And Small Businesses Face More Serious Repercussions From Cyber Threats

The Next Palantir Could Ignite in Tel Aviv — Built on Soft-Signal Intelligence, Not Surveillance

Automation Is the Essential Part of Cyber Risk Management: Know Why

‘It’s Never Too Late To Learn a New Language’: Yair Solow, CEO of Centraleyes

The Benefits of SOC Reports for Cybersecurity

What Is Cyber Risk Quantification?

Large And Small Businesses Face More Serious Repercussions From Cyber Threats

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps