What are anomaly detection algorithms?

Anomaly detection algorithms

Anomaly detection in cybersecurity is the process of identifying patterns or behaviors in network, user, or system activity that deviate from established baselines. Anomalous behavior in an environment could translate into a serious threat capable of disrupting network operations. As the attack surface of modern environments is growing in complexity, advanced anomaly detection systems are important for detecting early signs of potential security incidents.

In this page, we will explore the different types of algorithms used, their challenges, and cybersecurity use cases in detail.

Types and techniques of anomaly detection

Anomaly detection can be classified into three types:

Point anomaly: A single data point that completely differs from the rest, such as a sudden traffic spike or an unexpected increase in login attempts over a short period, indicating a possible brute-force attack.

Contextual anomaly: A data point that differs from the rest in a specific context, such as an unusual login time for a user or a large file transfer happening late at night rather than during business hours.

Collective anomaly: A group of data points collectively deviates from normal patterns, like a sudden increase in privileged user access across multiple servers, signaling potential coordinated insider actions. There are several ways in which you can find these anomalies by using different models. These techniques include:

Statistical methods: These methods use mathematical models like Z-score, interquartile range (IQR), and Grubbs’ test to identify outliers based on data distribution assumptions. They are simple and fast but less effective for complex patterns.

Machine learning methods: Algorithms like isolation forest, support vector machines (SVM), and clustering learn normal behavior from data and detect deviations. These methods are adaptable and handle complex data better, but require tuning.

Deep learning methods: Using neural networks, such as autoencoders and convolutional networks, you can detect subtle and complex anomalies in large, high-dimensional data.

Statistical methods

Statistical methods in anomaly detection use mathematical and statistical techniques to identify data points that significantly deviate from expected norms. These methods establish baseline patterns based on historical data and flag any data points outside defined thresholds as anomalies. Common statistical methods include:

Z-score: Measures how far a data point is from the mean in terms of standard deviations. Points with high Z-scores are considered anomalies.

Interquartile range (IQR): Uses the spread of the middle 50% of data to identify outliers falling outside the normal range.

Grubbs’ test: Detects a single outlier in data that follows a normal distribution by testing the most extreme value.

Statistical methods are simple, fast, and easy to interpret, but they are limited by assumptions about the data distribution and may not handle complex, non-linear patterns well. That's why it's difficult to find contextual and collective anomalies in your network with these methods.

Machine learning methods

Machine learning (ML) methods are often the most effective for identifying complex anomalies for several reasons. They excel at analyzing large volumes of data with multiple variables, revealing subtle, non-linear relationships and hidden anomalies that simpler approaches may fail to recognize.

They also learn normal behavior and tune their detection criteria to reduce alerts caused by benign deviations, enabling security teams to focus on true threats. Some of the common types of ML-based anomaly detection categories include supervised and unsupervised learning.

Supervised learning involves training an ML model on labeled data, where each input is paired with the correct output. The model learns to recognize patterns by comparing its predictions to the known labels and adjusting itself to improve accuracy.

This method is widely used for classification tasks like identifying malware, spam emails, or phishing attacks by learning from historical examples. Its strength lies in generalizing from past labeled data to detect known threats efficiently. However, supervised learning relies heavily on the availability of high-quality, well-labeled data to perform effectively.

Unsupervised learning, on the other hand, finds patterns and relationships in unlabeled data without prior knowledge or guidance. It groups data based on similarities, enabling detection of unknown or new anomalies without needing pre-labeled examples, making it ideal for cybersecurity threat detection.

Let's explore the different types of supervised and unsupervised learning methods in detail:

Support vector machine (SVM): This method works by finding the optimal hyperplane that separates data points into classes (e.g., malicious vs. benign) while maximizing the margin between classes. It handles high-dimensional data well, making it effective for malware detection, intrusion detection, and spam filtering. SVM can classify complex patterns, enhancing threat detection accuracy. However, it may require careful tuning and can be computationally intensive for very large data volumes.

Random forest: This ML algorithm builds multiple decision trees using random subsets of data and features. Each tree makes its own prediction, and results are combined by majority vote (classification) or averaging (regression). This ensemble approach improves accuracy and works well on complex, large datasets. It can identify and flag attacks that achieve unauthorized access to a system by leveraging its vulnerabilities.

K-means clustering: This is an unsupervised learning algorithm that partitions data into K clusters based on similarity. It starts by randomly initializing K centroids, then assigns each data point to the nearest centroid using Euclidean distance. Next, it recalculates the centroids as the mean of assigned points and repeats assignment and centroid update until clusters stabilize. You can detect anomalies like unexpected user behavior or system activity deviating from normal clusters.

Isolation forest: This is an efficient unsupervised anomaly detection algorithm used widely in cybersecurity. It isolates anomalies by randomly selecting features and splitting values to recursively partition data, creating many decision trees. Anomalies are easier to isolate and thus have shorter path lengths in these trees. The algorithm calculates anomaly scores based on these path lengths.

It effectively detects anomalies like unusual login attempts, network intrusions, and malware activity by identifying data points that differ significantly from the majority.

Principal component analysis (PCA): This dimensionality reduction technique transforms complex, high-dimensional data into fewer variables called principal components, which capture the most significant variance in the data. By simplifying data while preserving essential patterns, PCA helps uncover underlying structures, trends, or outliers. Detect irregular system activities by identifying data points that deviate strongly from principal component patterns, indicating potential threats or breaches.

Density-based spatial clustering of applications with noise (DBSCAN): This is a clustering algorithm that groups closely packed data points into clusters based on their density and labels points in sparse regions as noise or anomalies. It defines clusters using two parameters: eps (radius to search neighbors) and minPts (minimum points required for a dense region) and identifies core points (dense areas), border points (edge of clusters), and noise points (outliers). It excels in finding oddly shaped clusters and handling noisy data, making it effective for real-world cybersecurity anomaly detection.

Deep learning methods

Deep learning methods for anomaly detection utilize neural networks with multiple layers to learn complex patterns and features from large, high-dimensional data. These methods excel at identifying subtle and sophisticated anomalies that traditional techniques might miss.

Key deep learning approaches include:

Autoencoders: This type of neural network learns to compress input data into a lower-dimensional representation and then reconstruct it back to its original form. Trained primarily on normal data, they excel at capturing typical patterns. When an input deviates significantly from these patterns, the reconstruction error increases, signaling an anomaly.

By identifying deviations from learned normal patterns, they enable the detection of new, unseen attacks in real time, helping reduce false positives and increasing detection accuracy.

Recurrent neural networks (RNNs): This method handles sequential data by maintaining a memory of previous inputs through loops in their architecture. This allows them to analyze time-series data, such as network traffic or user activity logs, capturing temporal patterns and dependencies important in cybersecurity.

Generative adversarial networks (GANs): These deep learning models comprise two components, a generator that creates synthetic data resembling real data, and a discriminator that distinguishes between real and fake data. Through adversarial training, the generator improves in producing realistic data, while the discriminator enhances its ability to detect discrepancies.

Convolutional neural networks (CNNs): These are specialized neural networks designed to process structured grid data like images. They use convolutional layers with filters that scan the input data to detect local patterns such as edges, textures, and shapes. These layers preserve spatial relationships by focusing on small regions of the input, enabling CNNs to learn hierarchical features from low-level edges to high-level objects.

Challenges in implementing anomaly detection

• Traditional anomaly detection algorithms often struggle with distinguishing between genuine anomalies and normal variations in behavior. Network traffic patterns, user behavior, and system performance can vary significantly due to several legitimate factors. This leads to alert fatigue and reduced trust in the system.
• Determining what constitutes normal behavior is inherently difficult. Organizations have complex, dynamic environments where normal patterns evolve continuously. Seasonal variations, growth patterns, and technological changes mean that static baselines quickly become obsolete.
• Raw anomaly detection lacks business context. A spike in database queries might be anomalous from a statistical perspective but perfectly normal during certain hours. Without understanding the operational context, systems generate irrelevant alerts.
• Processing massive volumes of security data in real-time while maintaining detection accuracy requires significant computational resources. Many anomaly detection techniques have become computationally expensive at an enterprise scale.
• Effective anomaly detection requires clean, consistent data from multiple sources. Organizations often struggle with data normalization, missing logs, and inconsistent timestamp formats across different systems.

How SIEM systems help bridge these gaps

Modern SIEM solutions are designed to address these challenges by:

• Bridging anomaly detection gaps through sophisticated correlation and contextual analysis across multiple data sources, suppressing false positives by recognizing legitimate business activities.
• Enhancing ML with rule-based logic and encoding domain knowledge to filter and prioritize events effectively.
• Implementing adaptive baselining that automatically adjusts to any changes, seasonal patterns, and evolving behaviors.
• Integrating with threat intelligence feeds distinguishes generic anomalies from known attack patterns, while comprehensive workflow capabilities enable automated incident response and containment actions.
• Normalizing diverse data sources and enriching events with asset information, user roles, and geolocation context, transforming raw statistical anomalies into actionable security intelligence that supports effective operations.

How ManageEngine Log360 enhances your enterprise security operations with advanced anomaly detection

ManageEngine Log360 is a comprehensive SIEM solution that transforms security operations with its re-engineered anomaly detection system that adapts to your organization's environment. The solution combines real-time correlation, MITRE ATT&CK®-mapped rules, and ML-powered detection algorithms to proactively combat threats and maximize your security posture.

Log360 leverages ML-powered user and entity behavior analytics (UEBA) to enhance anomaly detection. It builds dynamic baselines of normal activity for every user and entity in the network, continuously learning from behavior patterns. When deviations such as unusual logon times, excessive login failures, or abnormal file access occur, they are immediately flagged as anomalies and assigned a risk score.

The soultion's UEBA module uses a sophisticated peer grouping system to detect anomalous behavior by comparing users against relevant baselines. Those groups include:

Time-based groups: These cluster users based on temporal patterns like work hours, days of the week, or seasonal activities.

Count-based groups: Users are clustered by volume of activities like file downloads, login attempts, or email sends.

Anomaly-based groups: These form around specific behavioral patterns or risk profiles that have been identified as potentially concerning.

This peer-based approach makes the system more accurate. It accounts for the fact that what's normal varies significantly between different user types and contexts.

Log360’s anomaly detection algorithms incorporate seasonality awareness to differentiate legitimate recurring patterns from real threats. By understanding behavioral patterns across hours, days, and weeks, it minimizes false positives, enhances risk scoring, and ensures that only true deviations trigger alerts.

For example, an organization’s database team performs large data exports every quarter for reporting. Traditional anomaly detection might flag this as abnormal due to high data transfer volume. Log360’s seasonality-aware algorithms recognize the quarterly pattern, suppressing noise and surfacing only unexpected data movements.

Enhance anomaly detection accuracy further by consolidating risk scores across platforms with Log360's user identity mapping, which links disparate user accounts (e.g., Windows, Linux, SQL) to a single base identity (e.g., AD). This ensures anomaly detection models treat all actions as belonging to a single user and just one representation of a risk score.

With Log360, you can easily manage security events by creating incident response workflows against them to automate responsive actions such as shutting down compromised devices, disabling USB ports, changing firewall rules, or deleting compromised user accounts. This helps in handling anomalies faster by reducing the mean time to respond and thus minimizing business impact while strengthening overall security posture.

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