Summary: This article covers everything you need to know about anomaly detection and why anomaly detection is important for your organization’s security. You’ll learn about common challenges companies face when detecting anomalous data, types of anomaly detection, and anomaly detection methods your company can leverage. By the end of this article, you’ll know how to find anomalies in data and prevent irregular data incidents with practical anomaly detection models.
What is Anomaly Detection?
Anomaly detection is the process of analyzing company data to find data points that don’t align with a company's standard data pattern. Companies use anomalous activity detection to define system baselines, identify deviations from that baseline, and investigate inconsistent data.
In cybersecurity, experts define anomaly detection as a monitoring feature of data observability tools that leverages machine learning to identify unexpected changes in a dataset. Once an anomaly detection system determines what data patterns to expect from applications, networks, and databases within your IT infrastructure, the system regularly scans data inputs and outputs to see if they align with the baseline.
When the system finds outlier data that deviates from the established pattern, it alerts administrators of the change and may take predefined automated actions, like suspending a user session or shutting down a system. Alerts help teams track system health, prevent security incidents, and speed up mean time to detection (MTTD) for security threats.
What is an anomaly?
To fully answer “What is anomaly detection?”, it is necessary to define what an anomaly is.
A data anomaly is any data point or suspicious event that stands out from the baseline pattern. When data unexpectedly deviates from the established dataset, it can show an early sign of system malfunctions, breaches, or newly-discovered security gaps. An anomalous data definition includes any inconsistent or redundant data points—including incomplete data uploads, unexpected data deletions, or data insertion failures—within a database.
Data anomalies don’t always signify an issue, but they are all worth investigating to better understand why a deviation occurred and if that anomaly is a valid point within a dataset.
Why Is Anomaly Detection Important?
With so much data across a company’s IT infrastructure, it’s impossible for companies to manually monitor all the inputs and outputs stored in or moving between their systems. Most companies leverage data mining to find trends that indicate their systems and security controls are operating normally.
Anomaly detection in data mining allows security teams to see imperceptible events or data points that show a statistically significant deviation from normal operating patterns. Often, teams need real-time data monitoring capabilities to respond to data abnormalities and possibly prevent a breach, detect fraud, or assess system health. Anomalous data points serve as the breadcrumbs that help teams find the source of security issues as fast as possible.
Companies need anomaly detection to assess security risks, investigate gaps, and strengthen their security posture to avoid data exposure.
Anomaly detection for SOC 2 compliance
Since data breaches pose a significant compliance risk, many organizations use an anomaly detector as part of their compliance strategy. SOC 2 compliance requirements include security anomaly detection tools as a vital element of security operations.
Anomaly detection models can track the ongoing success of security controls and ensure data is stored, accessed, and moved securely. Plus, these types of anomaly detection tools use logs and offer reporting capabilities to demonstrate data anomalies during security audits. This reduces the risk of violating regulatory compliance requirements and data privacy laws.
Anomaly Detection Challenges
Anomaly detection in data science is only valuable if it can identify true outliers, which means teams must train the system before it can be useful. Otherwise, the system can relay an excessive number of alerts beyond what a team could feasibly investigate.
It takes time for an anomaly finder to establish a reliable baseline for data across a company’s entire IT infrastructure, especially if a team doesn’t have pre-existing labeled data sets for the system to learn from.
Data quality issues and small training samples also make anomaly detection algorithms less effective. Without a high-quality dataset to reference, the system develops unreliable anomaly detection, meaning that the model can miss glaring outliers. Alternatively, anomaly detection systems can also be too sensitive if they aren’t provided enough data to determine what degree of deviation from the norm defines a true outlier.
How Does Anomaly Detection Work?
As with any solution using artificial intelligence and machine learning, an anomaly detection model needs some guidance to define normal data so it can identify what qualifies as abnormal. Companies teach anomaly detection tools how to do anomaly detection by providing training data in a sample set. From this data, the system develops an algorithm to detect irregular data.
However, not all companies have informative enough data to fully equip the anomalous activity detection algorithm to recognize a deviation. Machine learning allows the system to observe elements of your IT infrastructure to determine baselines and construct a more robust detection model.
Once the system establishes baselines for what the system data looks like when it’s operating properly, the security team defines limits to indicate how disparate a data point needs to be from the baseline to qualify as an outlier. Any time the algorithm detects data beyond these limits, it sends the administrator an “anomaly detected” alert.
Supervised vs. unsupervised anomaly detection
Most teams have sample sets they use to train the machine learning algorithm to detect anomalous data. Whether or not the data in these sample sets is labeled determines which of the two main anomaly detection types a system is—supervised or unsupervised.
Supervised anomaly detection involves training a model with pre-labeled data. These datasets contain predefined normal data and clearly labeled examples of anomalies. While this may make an anomaly detection platform better at identifying expected abnormalities in data, it won’t account for abnormalities security teams don’t anticipate or haven’t seen before. Plus, many labeled datasets don’t contain enough outlier data to effectively train the algorithm.
Most organizations don’t have pre-labeled data, so they do unsupervised anomaly detection to define system baselines. Teams may provide the algorithm with unlabeled data sets and allow the system to determine what data qualifies as outliers, or they may allow the algorithm to form organically by observing a system at work. With each alert, these teams will teach the system what data points are normal and abnormal, which can be time and resource intensive.
Anomaly Detection Examples
One of the clearest anomaly detection examples is for preventing fraud. For example, a credit card company will use anomaly detection to track how customers typically use their credit cards. If a customer makes an abnormally large purchase or a purchase in a new location, the algorithm recognizes the anomaly and alerts a team member to contact the customer. The system may also automatically block a suspicious charge.
In cybersecurity, anomaly detection plays a major role in the Zero Trust security model. Data anomaly detection tools help evaluate risk and determine a risk score each time a user requests access to an application. The algorithm allows systems to rapidly consider multiple data points and determine whether to allow or deny access. When no anomaly is detected, the system can automatically provide access; when an anomaly is detected, it triggers an alert to the system administrator.
Network anomaly detection models can also track traffic and monitor the safety of an organization’s network security. Intrusion detection systems use anomalous data to alert administrators when an intruder attempts to breach the security perimeter.
Anomaly Detection Algorithms
Organizations can train their ML algorithms with a wide variety of methods for anomaly detection and prevention. Some of the most common anomaly detection techniques are:
- Density-based algorithms: these anomaly detection approaches determine outliers based on whether a data point deviates beyond the normal—and subsequently denser—data population. Isolation Forest is a popular example that creates decision trees from a dataset by randomly selecting characteristics to detect similarities and isolate outliers.
- Cluster-based algorithms: these methods assign data points to clusters based on detected similarities. K-means is a popular example, where outliers are determined by how far they extend from a cluster group.
- Bayesian-network algorithms: these methods work by defining the probability that an event will occur based on the presence of contributing factors and detecting relationships with the same root cause.
- Neural network algorithms: these methods use time-stamped data to forecast data patterns and identify outliers that don’t align with the historical data. Long Short-Term Memory (LSTM) is a popular example that defines a sequence of events and detects outliers that do not follow the sequence.
While not expressly common, non-statistical machine learning anomaly detection algorithms are also gaining popularity as an alternative method for detecting anomalous data in complex network environments.
How StrongDM Simplifies Anomaly Detection
Maintaining a Zero Trust Architecture involves deeply understanding how users regularly move through your IT infrastructure. However, businesses can’t know what access is risky without insight into normal access patterns.
StrongDM’s Dynamic Access Management (DAM) platform allows organizations to manage user access with confidence. Our platform records comprehensive logs to track and understand normal user access patterns to inform a user anomaly detection model. This data helps companies identify which access requests are safe and which pose a security risk to your organization.
Plus, StrongDM provides near-instant risk assessment capabilities and exceptional visibility across the entire IT infrastructure to audit usage and grant or revoke access just in time. This gives both regular and new users a streamlined access experience without compromising security.
Make Access Security Easy with StrongDM
Finding security risks doesn’t have to be like searching for a needle in a haystack. StrongDM makes it easy to detect abnormal user behavior and keep your network secure. With StrongDM, your organization has full visibility into use patterns across your entire IT infrastructure.
Detect anomalies before they become a problem. Get a free no-BS demo of StrongDM today.
