What is anomaly detection and why it is used?

The anomaly detection or outlier detection is a process of identifying the deviations from the original datasets by finding the interesting facts about the monitored processes and provides valuable information about the data. It is essential to identify outliers in the system to prevent malicious activity, operational failures, and fraudulent behaviors. 

The anomaly detection is used in various areas such as fraud detection, damage detection, surveillance and more. A machine learning algorithm is used to automate the anomaly detection process. 

Auto anomaly detection 

Auto anomaly detection is a very important aspect in current IT enterprises where a large volume of data makes it difficult to detect outliners through the manual process. Usage of anomaly detection in IT operations will prevent revenue loss, retain the brand name, and trigger swift troubleshooting. To meet the business standards, the enterprise is adopting the anomaly detection method to monitor their business, product, and progress. 

Once the anomalies are detected, the alerts are sent to the IT operators to make prompt decisions to resolve the incidents. Detecting anomalies is very essential to run IT operations smoothly, identify interruptions that may happen in the future, prevent incidents to stop unnecessary resource usage. To yield better results and avoid false positives, the anomalies should be detected earlier

By adopting the AIOps platform, you can avoid manual monitoring and reduce alerts. Earlier the human intervention was required to detect anomalies within IT operations; now the advent of AI easily detects the anomalies without manual intervention. 

Time series anomaly detection 

Time series anomaly detection is static data with start and end limit that carries a certain time interval. In time-series data, the ultimate aim is to detect anomalies with respect to the time. There are also other scenarios to be considered while handling the anomalies which include stationary, trend and seasonality. Seasonality is considered as a repeated pattern which occurs after the time interval. The trend is a gradual and datasets will increase over a period of time.  

Stages in anomaly detection 

  1. Understanding the business 
  2. Getting as much data in different varieties as you can 
  3. Data cleaning and enriching the data 
  4. Remove affected data that produce false positives
  5. Visualize the data especially during large datasets 
  6. Use live datasets to make real-time predictions 

The anomalies are broadly categorized as:

  • Point anomalies: It is the simplest anomaly category where one object observed against the other objects. 
  • Contextual anomaly: If the object is found to contain anomalies in any defined context, it is called a contextual anomaly.
  • Collective anomalies: If any linked objects observed against other objects as anomalies. The collection of objects is called collective anomalies. 

Approaches in anomaly detection 

  • Supervised anomaly detection: This process requires a dataset labeled as “normal” and “abnormal” and also requires training a classifier.  
  • Semi-supervised anomaly detection: This set up uses training and test datasets where training data consists of normal data without anomalies.
  • Unsupervised anomaly detection: It is a set up that we are not sure about what is normal or what is abnormal. This approaches the data only based on the natural features of the datasets.  

Why your business needs anomaly detection?

  • You can monitor any datasets including data source, user logs, networks, devices, and servers.
  • Swiftly identifies the unknown security threats 
  • Identifies the unusual behaviors of the data sources that cannot be found by traditional methods.
  • Stops the alerts to find the key anomalies 
  • You can discover anomalies in event streams including web traffic and historical data.
  • Find various data features including users, hosts, and agents. 
  • Effortlessly handles huge datasets 

Autointelli AIOps platform automatically detects potential problems in complex IT systems without human interference. It aids in managing large systems, improves customer experience, maintains team productivity and helps to stay competitive. 

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