In today’s data-rich world, finding anomalies in your data can be time-consuming. Using AI-driven anomaly detection, digital teams can eliminate this repetitive task and focus on performance-driving strategies instead.
Anomaly detection can identify outliers, threats and trends in your data. These can help you detect network intrusions, spot fraudulent transactions, and prevent security breaches.
Machine learning (ML) algorithms are used to identify anomalies and predict patterns in data. They are also used in various other applications, including fraud detection and online banking.
One of the capabilities of a web application firewall example is anomaly detection. Supervised anomaly detection methods require training and test datasets tagged with standard and anomalous values. These techniques have been applied to various use cases, such as fault detection in manufacturing and network monitoring.
Unsupervised anomaly detection algorithms use raw data without labels to find anomalies. They are often based on various statistical models, such as k-nearest neighbors and Bayesian networks.
Explainability is a critical aspect of outlier detection. Understanding what the algorithm sees as an outlier and how it relates to the application context is essential.
The explanation can be helpful to the users, as it allows them to make informed decisions about which outliers to correct or delete. An analyst may be interested in an outlier in credit card fraud detection. For instance, since it deviates from the norm and may indicate illegal activity.
Understanding the context of the data and the business use case is critical for outlier analysis. ML algorithms can help detect anomalies that are not likely to impact the outcome of the investigation. Still, if there is a chance that an outlier will alter the result, then you should consider whether it should be accepted, corrected or deleted.
AI can help organizations identify threats that may be able to cause significant damage. It can also help businesses avoid data breaches by detecting malware and other security issues before they occur.
Artificial intelligence helps security teams sift through massive amounts of data, finding patterns that can be difficult for humans to identify. It reduces time and costs, allowing security analysts to focus on identifying lower-priority alerts.
For example, suppose a financial institution receives a suspicious email. In such a situation, it may evaluate the message using AI and ML algorithms to identify whether it is an honest communication or a phishing effort. It can then alert the security team to investigate the email and take action to prevent further attacks.
Another area where AI and ML algorithms can help is fraud detection. By analyzing data from different sources, such as email, chat, and social media, AI can help identify fraudulent activities that could cost business money.
Using AI and ML in cybersecurity also improves incident response and security operations, enabling security teams to respond more quickly to threats. It also helps organizations prioritize and automate security responses, reducing human analysts’ workload.
The biggest challenge with using AI in anomaly detection is acquiring and training the AI system. Typically, this requires large data sets of malware codes, non-malicious codes and anomalies to prepare the system. With this data, the system may make correct results and false positives.
Identifying trends is one of the most critical parts of anomaly detection. Timely detection of trends helps you identify opportunities and threats and prevent problems from developing into more significant issues.
Understanding the characteristics of typical behavior in depth is necessary for trend detection. Modeling normal behavior enables AI algorithms to recognize anomalies within data, which can help you improve your analysis and reduce false positives.
Trends in data often occur because of a combination of different factors, such as seasonality and noise. These can be challenging to distinguish from real outliers and may require subject matter expertise or specialized tools to separate the two.
Another common challenge in identifying trends is determining the correct context for the measured metric. For example, if a metric is related to power consumption, but there is an unexpected increase in demand for umbrellas outside peak demand periods, that could be a contextual anomaly.
Anomaly detection algorithms rely on statistical metrics to find outliers in data, such as a standard deviation over recent windows of time series or a control chart on a signal. However, these techniques are more challenging to apply to multivariate data and less robust than machine learning approaches.
The ability to recognize patterns in data is crucial for AI-powered anomaly detection. Machine learning algorithms help identify patterns, predict when the pattern is about to break and alert users when a problem has been placed.
There are many ways to identify a pattern in data, including statistical, syntactic, and neural networks for pattern recognition. Each technique has its pros and cons.
Statistical pattern recognition (StatPR) is an approach that takes a sample of data and determines its pattern vectors by measuring its features. This method is often used to solve recognition problems that involve a large amount of data.
Neural network-based pattern recognition is the most common type of ML for this purpose. This method uses a complex system of artificial neurons that can learn to recognize new data points and make predictions about them.
It is an essential technique for anomaly detection, especially in dynamic environments where human resources are only sometimes available to manage the complexity of containers and microservices. It helps automate identifying anomalies and avoids alert storms, false positives, and other issues that can occur when static thresholds are applied.
It’s also helpful for determining whether or not a data point fits into a predefined pattern or if it is an anomaly and should be treated differently. It can help with fraud detection, identifying hidden data groups, surfacing insights, and other use cases that require AI to find information within large unstructured data sets.