What is Anomaly Detection?

05 Mar What is Anomaly Detection?

In the world of data mining, anomaly detection is the process of identifying points of data, events, or observations that are different from what’s considered normal within datasets – or, in simpler terms, outliers.

The detection of anomalous data often means that certain events need immediate attention. These scenarios could be potential opportunities like a shift in the trends within a market or a glitch within the anomaly detection system that has to be addressed quickly.

Detecting anomalies has become crucial for organizations in various industries especially given that their identification typically leads to positive outcomes.

Today, network anomaly detection centers on the use of statistical tools including other techniques that could help analyze datasets or metrics. Machine learning continues to play a big part in this, and organizations are increasingly involving it in the process.

What is Anomaly Detection in AI?

Anomaly detection in AI refers to the identification of data points that do not conform to the expected behavior. This can be useful for identifying fraudulent activity, detecting hardware failures, or flagging unusual patterns in financial data.

There are a variety of methods for anomaly detection, but they all share the goal of identifying outliers in a dataset. Common techniques include clustering, statistical analysis, and machine learning.

Anomaly detection is an active area of research, and new methods are constantly being developed. However, anomaly detection is not without its challenges. In particular, it can be difficult to determine what constitutes an anomaly, and false positives are often a problem. As a result, anomaly detection is an important tool for data analysis, but it should be used with caution.

Anomaly detection in AI provides a number of benefits including:

• Automation: Anomaly detection algorithms that are powered by artificial intelligence are capable of going through datasets dynamically. This allows them to tweak important parameters to reveal opportunities while identifying potential issues.
• Data accuracy: AI can accurately pinpoint and notify users whenever worthwhile anomalies have been detected, minimizing the potential of false positives/negatives from being triggered.
• Meticulousness: Normally unnoticeable by human operators, AI-powered anomaly detection systems can provide monitoring that catches even the most minute details.
• Real-time data analysis: Anomaly detection systems can detect and process data in real-time. These platforms can instantly recognize anomalies and send out the appropriate triggers as needed.
• Self-learning capabilities: Self-learning is perhaps one of the best traits of AI. When incorporated into anomaly detection systems, these platforms can learn and provide answers when users require them.

What is the Purpose of Anomaly Detection?

If you are learning about anomaly detection, the term itself may sound quite intimidating due to its technical nature. The good news is that advances in artificial intelligence (AI), deep learning, and machine learning have made anomaly detection more accessible and straightforward.

To give you an idea of how it can benefit businesses, let’s take a look at some of the main reasons for using anomaly detection:

• Security breach prevention: Cybercriminals are constantly learning new exploits and improving their hacking techniques to overcome online security measures. With anomaly detection, security teams will immediately be notified whenever there’s an anomaly detected.
• Analyzing KPIs automatically: Many organizations are still relying on manual techniques when managing data throughout various dashboards. The problem here is that such tasks can become time-consuming, especially where big data is concerned. With anomaly detection, companies can rely on AI algorithms to help scan and automate KPI analysis – freeing up time for data experts to perform other key tasks.
• Improving organizational efficiency: Technology continues to improve each year, allowing for processes like anomaly detection to become accessible even for smaller entities. Today, even a minor company can use a network anomaly approach to increase its efficiency and bottom line.
• Identifying potential opportunities: Detecting anomalous data doesn’t always indicate a negative finding – it could also be a potential opportunity that businesses can take advantage of. Anomaly detection can help digital teams streamline their ability to find ways to enhance their company’s performance and give them more time to take advantage of these opportunities.

Types of Anomaly Detection Methods

Thanks to machine learning, anomaly detection is now available through different formats. These include:

Unsupervised Anomaly Detection

Unsupervised anomaly detection is perhaps the most common type today. It is used by many organizations especially because it requires minimal intervention compared to the supervised approach. A neural network is the most popular example of an unsupervised algorithm.

With artificial neural networks, there is less manual work required when it comes to processing the examples, primarily because there is no manual labelling required. A neural network can even be used when assessing unstructured data. Furthermore, such networks can identify anomalies within these datasets and use them to operate on other data.

In short, the primary benefit of unsupervised anomaly detection is its ability to minimize manual work.

Supervised Anomaly Detection

Supervised anomaly detection is when a machine learning expert uses their own training dataset to get things going. The items within their dataset are labeled as normal and abnormal, and they use these examples to identify patterns.

That’s why the overall quality and accuracy of a training dataset are important in supervised anomaly detection. However, it requires more manual work since an engineer has to be present when gathering and labeling the examples.

Semi-Supervised Anomaly Detection

The third type of anomaly detection is the semi-supervised approach. This method combines the best of both worlds offered by unsupervised and supervised systems.

In this approach, machine learning engineers often incorporate unsupervised techniques as a means to automate learning while working on unstructured data. The benefit here is that they can combine that with human supervision so that users can manage the patterns learned by the model.

The outcome in a semi-supervised system often results in more accurate model predictions.

Is Anomaly Detection Supervised or Unsupervised Learning?

Anomaly detection can be either supervised or unsupervised.

In supervised anomaly detection, the data is labeled as normal or abnormal. The machine learning algorithm then learns from this labeled data to identify new abnormal examples.

In unsupervised anomaly detection, the data is not labeled and the machine learning algorithm must learn to identify anomalies on its own. This can be more difficult, but it is also more generalizable since it can detect anomalies that were not seen in the training data.

Ultimately, the choice of whether to use supervised or unsupervised anomaly detection depends on the nature of the data and the type of anomalies that are being sought.

Common Examples of Anomaly Detection

Businesses now have access to various analytics systems that can help keep track of a wide range of metrics. Although this may seem like adding more work to human operators, it also presents numerous ways they can obtain insights, especially thanks to anomaly detection.

Today, there are three main use cases that organizations rely upon with this technology. These are:

User Experience

As soon user experience issues come up, anomaly detection platforms can inform users of their presence so they can immediately take action. Doing so can help them avoid problems that often lead to churn and lost opportunities. Anomaly detection, therefore, facilitates user experiences and is used in various industries today.

For instance, anomaly detection solutions can keep track of user segments, devices, and operating systems within the gaming industry. It helps ensure that errors and glitches are resolved immediately before they can cause negative outcomes.

Another example is when online businesses have to mitigate common problems such as server downtimes and glitches. With anomaly detection, these brands can ensure streamlined customer experiences across all of their channels.

Application Performance

The second most common application of anomaly detection is in enhancing application performance. After all, such an aspect can be the main facilitator or deterrent in workforce production.

In the conventional way of handling app performance, such monitoring techniques only allowed users to react as soon as issues arise. This kind of reactive approach often results in companies already experiencing the worst parts of the problem by the time they’ve identified it.

Anomaly detection helps detect and fix application performance concerns before they begin affecting users. The telco industry is one prime example that can benefit from such a capability.

For instance, telco operators require complex solutions that will help them mitigate problems that could affect their systems and customers. Using anomaly detection systems, telephone companies can proactively resolve performance problems as soon as they happen.

Product Quality

The third most common application of anomaly detection in the real world has to do with quality control. With products constantly requiring changes, there will always be behavioral anomalies present with every new version release.

The e-commerce industry is one of the prime candidates in quality control using anomaly detection. Product managers in this niche often have to keep track of their sales funnels, conversions, and other important metrics to improve their brands. Anomaly detection allows product quality concerns to be identified and resolved faster than the traditional way of managing such issues.

Another ideal example is the security needed by fintech companies, especially since financial stakeholders and customers typically require constant reassurance. For those in this sector, staying ahead of potential cybersecurity breaches is vital to prevent them from happening in the first place.

Conclusion

Anomaly detection can have a significant effect when applied correctly. Artificial intelligence, particularly machine learning, is already playing a key role in systems that detect anomalous datasets.

Some of the main reasons for using anomaly detection include:

• Preventing security breaches particularly with cybercriminals constantly learning and adapting new exploits.
• Automated KPI analysis allows organizations that used to rely on manual techniques to manage a wide range of data through different dashboards.
• Anomaly detection allows even smaller entities to improve overall organizational efficiency by streamlining existing processes.
• Platforms that use anomaly detection algorithms can streamline a digital team’s ability to improve their company’s performance by getting rid of manual tasks.

Today, one can consider anomaly detection as being similar to analytics. Many companies are already starting to use it and incorporate its methods into their systems.

Although the term itself may seem complex due to its technical nature, the integration of artificial intelligence has allowed anomaly detection to be more accessible and direct than ever. Key personnel can continue working as usual while their anomaly detection platforms will help them find ways to improve their processes.