What is anomaly detection?

Anomaly detection is the process of identifying data points in a dataset or system that fall outside the norm. During data analysis or through machine learning, anomaly detection will flag instances that do not conform to your usual patterns or statistical models within most of your data. Anomalies can appear as outliers, unexpected changes, or errors—it depends on the kind of data you are analyzing and any predefined parameters you have set. Anomaly detection is useful because you can target potential issues or threats quickly and efficiently and maintain the integrity and reliability of your system.

Here are a few types of anomalies that anomaly detection methods can find. (It is important to note that these categories are not mutually exclusive; anomalies can show characteristics from multiple categories at the same time.)

• Point anomalies are individual data points that are notably different from the rest of the data set. For instance, a sudden large credit card purchase that is out of the norm for a particular credit card holder would be a point anomaly flagged to investigate as potential credit card fraud.
• Contextual anomalies are when a data point’s normal behavior can vary depending on its context. A common example is a retail site experiencing a major increase in traffic and sales on Black Friday, the busiest shopping day of the year. Such a spike would be anomalous at other times of the year, so special parameters are set for this day.
• Collective anomalies involve a group of data points that exhibit anomalous behavior altogether even though the individual data points might look normal. These anomalies are identified by observing the relationships or patterns among multiple data points. A DDoS attack is an example of a collective anomaly because it creates traffic from multiple sources that differ from normal traffic patterns.
• Temporal anomalies and time series anomalies happen when there are deviations in the data in a temporal sequence, such as a sequence of events or seasonal change. For example, a shift in peak tourist season for a vacation destination, unusual weather patterns during a particular season, or a surge of traffic that is not at rush hour would all be temporal anomalies.
• Spatial anomalies and geographic anomalies involve anomalies that happen in spatial or geographic data. These anomalies can be found by looking at the spatial relationships between data points. For example, in public health data, an unusually high concentration of individuals diagnosed with a disease in a specific area would be a spatial anomaly that is investigated as a localized outbreak.

Anomaly detection is important because it helps you identify unusual patterns, behaviors, or events that may lead to problems down the line. Your organization can get alerted to potential risks, inefficiencies, and abnormalities in every system and data set that you choose to have it monitor. This gives you the information you need to intervene quickly and proactively before any problems escalate. Being aware of all these anomalies across various aspects of your business keeps things running smoothly, shows you places you can focus on improving, and protects you from both internal and external attacks.

Anomaly detection works by first establishing a baseline behavior profile. This profile represents the expected patterns and behaviors of the data when everything is functioning normally. It is usually created using historical data or a representative sample of normal behavior.

Once the normal behavior profile is established, new incoming data is compared to this profile. The data points are evaluated to see how well they match the expected characteristics of the normal behavior profile. Any data points that significantly deviate from it are flagged as anomalies. (These deviations can be identified using various statistical techniques, machine learning algorithms, or rule-based approaches that we will explain in the next section.)

Next, detected anomalies are further investigated to understand their causes and implications. It is important to validate the anomalies to make sure they are not false positives or outliers caused by measurement errors or random fluctuations. Once the anomalies are verified, it is time to take action. This might mean further investigation, maintenance or repairs, security measures, quality control adjustments, or any other steps that would lessen their impact.

Most anomaly detection techniques can be either rule-based or machine learning-based. The latter may be broken up into three machine learning-based groups: supervised techniques, unsupervised techniques, and semi-supervised techniques. Which technique you choose depends on the specific requirements of the problem you are trying to solve and how much labeled data you have.

Supervised ML anomaly detection techniques are techniques that require labeled data that clearly defines both normal and anomalous instances during training. The model learns the patterns of normal data and then classifies new data points as either normal or anomalous based on what it has learned.

Unsupervised ML anomaly detection techniques work without labeled data. They assume that anomalies are rare and significantly different from most of the data. These techniques aim to identify unusual patterns, outliers, or deviations from normal behavior.

Semi-supervised ML anomaly detection techniques use a combination of labeled and unlabeled data. They leverage the labeled data to establish a baseline of normal behavior and then identify deviations from this baseline using the unlabeled data. This is especially helpful when working with unstructured data.

While machine learning is commonly used in anomaly detection, it is worth mentioning that other techniques (such as statistical methods, rule-based approaches, and signal processing techniques) can also be utilized for anomaly detection. These non-machine learning techniques rely on different principles and algorithms to identify anomalies in the data.

Anomaly detection has a variety of use cases across different areas. Here are a few use cases you may encounter:

• Cybersecurity: Anomaly detection helps identify unusual patterns or behaviors in network traffic, system logs, and user activities. This helps you spot cyber threats like intrusion attempts, malware, and data breaches.
• Application and system monitoring: Anomaly detection is crucial for monitoring the performance of your applications, servers, and network infrastructure. It can help you avoid potential outages because it can quickly find and report anomalies in metrics like latency, CPU usage, and memory utilization.
• Fraud detection: Anomaly detection can zero in on credit card fraud and identity theft by spotting out-of-the-norm spending patterns, purchases made from unusual geographic locations, and other suspicious activities.
• Hardware maintenance: You can utilize anomaly detection to monitor how your hardware is doing as well. This includes factors such as CPU temperature, fan speeds, and voltage levels. It can flag anomalies that might mean impending failures are on the horizon so that you can start repairs before there is an outage.
• User behavior analytics: Anomaly detection can analyze user behavior patterns and trends in your applications, websites, and other online platforms while avoiding user profiling. It helps identify unusual user interactions and can also help personalize security measures.

Anomaly detection has numerous benefits. Here are just a few of the ways it can help your organization:

• It allows you to detect problems early before they become bigger issues. If you catch anomalies early on you can take action to stop possible damage or disruption.
• It plays a key role in identifying suspicious activities that might be malicious, such as cyberattacks or fraud. This allows you to quickly spot potential threats and improve your security.
• It can spot inefficiencies by looking for deviations from a system’s optimal performance. This can help you streamline your processes and come up with ideas for overall improvements to your systems.
• It is vital to customer service because it finds anomalies that could impact customer satisfaction, whether it is service disruptions or an unusual increase in customer service inquiries that may indicate a problem.
• Machine learning-based anomaly detection has the added benefit of being able to monitor your systems at all times.
• It can even help you with regulatory compliance because it can spot deviations from regulatory requirements as well as industry standards.

Anomaly detection techniques come with certain limitations and challenges. Here are some drawbacks to be aware of:

• Supervised machine learning models may require a lot of labeled data to work well. If the anomaly detection training model has an insufficient amount of labeled data its accuracy will suffer.
• Setting inaccurate thresholds for the anomaly detection algorithm to measure against can cause a variety of reporting errors.
• If the algorithm was trained on a historical model that is now out-of-date, the performance of the model may degrade. Updating the model may be laborious and time-consuming.
• Even with accurate thresholds, the algorithm can occasionally generate false positives (normal data points flagged as anomalies) or false negatives (anomalies not detected). Balancing the trade-off between these two types of errors can be hard since reducing one often leads to an increase in the other.
• Anomaly detection algorithms can be sensitive to noisy or irrelevant data. You can use preprocessing techniques and other noise reduction methods to mitigate this.
• Scalability can be a challenge when you are applying anomaly detection to large datasets or real-time streaming data. Your organization may need more computational resources than you currently have to process it all.

Anomaly detection best practices are important to follow to get the best results. Here are some you should keep in mind:

• Before you begin, be sure to gain a thorough understanding of your data’s patterns and characteristics.
• Establish baseline patterns or thresholds for expected behavior that are as accurate as possible. Do not skimp on this step—you will have problems down the road if you do.
• Choose an anomaly detection technique that is a good match for the kind of data your organization generates. (You can combine multiple techniques if that is the best approach.)
• Regularly monitor your anomaly detection system’s performance and update it if the data you are generating has changed.
• If you have questions, be sure to loop in members of your team who are experts in the subject matter that is being analyzed. They can help you interpret the results more accurately.

Elastic provides a clear, easy-to-use interface that utilizes a variety of machine-learning techniques to provide sophisticated real-time analysis of your data. Elastic’s anomaly detection features include step-by-step workflows to help you build a better anomaly detection machine learning job. It also includes visualizations that make it easier to understand your anomalies and what might be the root cause of them, plus reliable detection of abnormal behavior across your various domains.

Learn more about Elastic machine learning and how Elastic Observability can help your business today.

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