Being a security analyst can feel like being trapped in a Where’s Waldo book. You can find yourself staring at a data stream looking for something that “isn’t like the others.” However, as your organization collects and correlates more data from the environment, finding the Waldo can feel overwhelming.
In a modern IT environment, organizations have hundreds or thousands of devices, users, and data points that they need to correlate so they can identify normal network activity. They can ingest up to a terabyte of data daily which means they need to rely on analytics models to create baselines for normal activity so they can look for abnormal, or anomalous, behaviors.
By using anomaly detection with machine learning, organizations can identify subtle deviations that would otherwise go unnoticed, improving overall threat detection and response capabilities.
What Is Anomaly Detection?
Anomaly detection is the process of identifying data points, events, or observations that deviate significantly from the majority of the data. These deviations fail to conform to an expected pattern or other items in the dataset. Essentially, anomaly detection starts by defining “normal” then flagging activity outside this boundary.
While simple statistical methods can identify obvious outliers, they fail when trying to identify deviations across complex, non-linear relationships. Machine learning excels in this domain by building sophisticated analytics models to define normal behavior for accurately pinpointing events that fail to match expected behaviors.
What Are the Types of Anomalies?
Anomalies come in different forms, and understanding these variations enables organizations to select the methodology that best matches their security needs.
Generally, anomalies fall into three main categories:
- Point anomaly: Single data instance that deviates from the rest of the data, like an unusual login for detecting early-stage threat activity.
- Contextual or conditional anomaly: Data instances considered anomalous only within a specific context that might be normal in a different context, like an admin logging in at 3am from an unknown IP address.
- Collective anomaly: Collection of related data instances that are anomalous as a group, even though the individual data points may not be anomalous on their own, like identifying multi-stage attacks where individual events appear normal but chaining them together can indicate lateral movement or data exfiltration.
Most modern security-focused machine learning models evaluate all three anomaly types to catch everything from isolated suspicious events to complex attack patterns.
How Does Anomaly Detection Work?
A machine learning-based anomaly detection solution typically follows these steps:
- Collect and preprocess training data: Ingesting raw data then cleaning, normalizing, and transforming it into a suitable format to select the appropriate attributes.
- Train machine learning model on historical dataset: Training to understand underlying structure, relationships, and statistical properties of the normal data points, like teaching a deep learning autoencoder to understand network traffic patterns.
- Deploy model: Using a trained machine learning model to analyze new, incoming data, assess it against the understood norm, and flag violations as anomalies.
What Are the Most Common Anomaly Detection Techniques?
The techniques largely fall into three categories: supervised, unsupervised, and semi-supervised.
Unsupervised Anomaly Detection
Unsupervised anomaly detection does not require pre-labeled data. It assumes that anomalies are rare and statistically different from the normal data points. Some examples of this technique include:
- Clustering-Based Methods (DBSCAN, K-Means): Grouping similar data points into clusters and considering ones that are different from the group’s average center as anomalies.
- Density-Based Methods (Local Outlier Factor): Considering data points that are close to one another as normal and defining anomalies as a data point that sits alone as the anomaly.
- Isolation Forest: Isolating data points that can be separated from the rest of the dataset through fewer random splits, making them easier to “isolate” than normal behavior.
- Autoencoders: Treating data that it cannot accurately reconstruct due to a high reconstruction error as an anomaly.
Supervised Anomaly Detection
Supervised anomaly detection requires a labeled dataset containing normal and abnormal data, acting as a standard binary classification task where the model distinguishes between the two classes.
Classification algorithms, like support vector machines, compare the normal and abnormal to flag anomalous events as “malicious” or “high risk.”
Semi-Supervised Anomaly Detection
Semi-supervised anomaly detection is a model trained on a dataset containing only normal data. Once the model learns what constitutes normal, it flags any data that fails to conform to this model as an anomaly. This machine learning model is useful when the organization can collect a pure, normal dataset but struggle to label anomalies.
What Are Some Cybersecurity Use Cases for Anomaly Detection?
In cybersecurity, where threats are constantly evolving, anomaly detection enables organizations to proactively identify potential security incidents and mitigate them before they can cause significant damage.
Network Intrusion Detection
Machine learning models can establish a baseline of normal network traffic patterns, including protocols used, data packet sizes, and communication frequency between devices. Deviations from this baseline, such as an unusual data transfer to an external server or a sudden spike in traffic from a single IP address, can indicate a security breach, malware activity, or a denial-of-service (DoS) attack.
Malware and Zero-Day Threat Detection
Signature-based antivirus software can only detect known threats. Anomaly detection models can identify previously unseen, zero-day malware by analyzing system behavior. Unusual patterns of file access, registry modifications, or outbound network connections that deviate from normal application behavior can signal the presence of malicious software.
Insider Threat Identification
Rule-based access controls can only catch known policy violations. Anomaly detection models can surface previously unseen insider risks by analyzing deviations in user behavior over time. Unusual patterns of file access, privilege escalation attempts, or logins at atypical hours that differ from a user’s normal activity profile can indicate potential insider misuse.
Best Practices When Evaluating Machine Learning-Driven Anomaly Detection
Dynamic environments generate high volumes of data, often leaving security analysts with alert fatigue when they spend time on false alerts. Anomaly detection that uses machine learning improves alert quality and enables security teams to proactively identify potential high-risk activity. When evaluating solutions, organizations should consider the following capabilities.
Behavioral Baseline Accuracy
Some solutions can automatically identify normal from across the organization’s users, hosts, applications, and network traffic. These strong baselines reduce false positives, especially when they automatically adapt to the environment over time instead of relying on static rules.
Real-Time Detection with High-Fidelity Alerts
As the organization’s environment changes, the solution should continuously score events, immediately surface anomalies, and provide context-rich alerts mapped to meaningful security or performance indicators. These high-quality alerts shorten investigation time and enable teams to detect issues before they escalate.
Automated Event Enrichment
Machine learning models rely on their data, including its context. When evaluating a solution, organizations should look for ones that enrich events with metadata that improve detection accuracy and streamlines response, like:
- Usernames
- Host details
- Geolocation
- Asset classification
- Threat indicators
Flexible Data Routing and Tiering
Machine learning workloads require fast access to recent, high-value data and long-term storage for historical pattern analysis. Tools with intelligent routing and storage tiers keep performance high while managing costs.
Visualization and Investigation Workflows
Security teams should be able to use dashboards, anomaly timelines, and investigation pivots for easy insight into behavior shifts, anomaly impact, and root causes. At a glance visibility enables analysts to review the machine learning-driven findings more easily.
Compatibility With Existing Security and IT Workflows
The solution should integrate with ticketing systems, incident response processes, change management tools, or SIEM/SOAR ecosystems. This interoperability is critical when trying to reduce time spent on repetitive security tasks and improve incident response speed.
Graylog Security: Anomaly Detection with Machine Learning for Improved Security
Graylog Security’s machine-learning–driven anomaly detection turns routine log data into early warning signals by automatically learning normal behavior and surfacing deviations that matter. By pairing behavioral baselines with enriched event metadata and real-time scoring, teams can quickly spot unusual user actions, suspicious system changes, or emerging performance issues long before they escalate.
With dedicated anomaly indexes, intuitive dashboards, and alert workflows built around high-confidence signals, organizations gain a clearer, faster path from detection to action. Instead of chasing noise, teams get a focused stream of insights that strengthen security, streamline investigations, and keep operations running smoothly.
To see how modern anomaly detection can elevate your visibility and response, request a demo and explore what next-generation monitoring looks like in practice.
