How to use AI for anomaly detection
Anomaly detection in AI means discovering abnormal deviations from the pattern of the datasets. This enables businesses to root out anomalies on time.

How to use AI for anomaly detection

Data is like oil that keeps the engines of the Industrial Internet of Things running. With Artificial Intelligence, processing vast amounts of data is no longer an insurmountable task. Machines do this at thousands of times the speed of humans. Industries can make quicker decisions of greater accuracy.

For efficient and quick data processing, one of the main objectives achieved is the identification of changes or discrepancies in datasets,in other words, anomaly detection.

What is anomaly detection 

 As humans, we are conditioned from birth to distinguish between the normal and abnormal in the world we inhabit. For example, in a basket of apples, the presence of a single orange immediately leaps to the eye. Similarly, Anomaly detection in AI means discovering abnormal deviations from the pattern of the datasets.  

In the process, the machine first recognizes a pattern as “normal”, and on this basis, roots out the events that do not adhere to the pattern. Setting the “normal” itself is a highly complicated process, given that datasets may be labeled or unlabeled

Why is it important?  

With AI, many industrial operations that can safely be entrusted to machines are rapidly becoming automated. Under this new climate, understanding how these machines work is important for industries to get a clear picture of their business.  

Data inflow happens at a rapid pace, and anomaly detection is essential to detect potential risks or threats to security; a basic example is multiple failed attempts at a log-in. The faster these risks are found, the safer the operations. The detection of these aberrations helps in analyzing their nature and cause, whether they are deliberate or otherwise. 

Why manual anomaly detection is no longer a solution? 

 A delay of even one minute in anomaly detection can mean a loss of millions of dollars to big concerns such as Google. Manual detection takes up time, and time is money in business. Statistical Process Control or SPC is the standard system by which statistical tools are used to control and monitor industrial processes.   

Data that do not fall within the limits set by SPC are considered potential causes for problems in the production process and are followed up for solutions. While SPC is a sound system, it cannot survive on its own. Here are major reasons why manual detection and SPC fall short.

  • Growing data volume makes the classification of relevant and irrelevant data a highly complicated task that cannot be managed by human analysts  
  • High competition means businesses must constantly stay on edge, and the best data analytics helps to achieve this by faster threat identification, improving operational efficiency   
  • Fraudulent activities are becoming harder and harder to track, with fraudsters developing better ways to do their work. There is a need for better fraud detection mechanisms that can be addressed in the best way only through AI-enabled smart machines    

What can AI do to automate anomaly detection? 

 AI and SPC when combined become a powerful tool for anomaly detection. With smart machines, greater accuracy and precision are ensured.   

 AI lifts a great load off human resources, which are too limited in availability to handle the complex workings of the cloud infrastructure.   

 Some of the major tasks AI performs are  

  • Automation of anomaly detection in data analytics and improving the “normal” standard of datasets, thereby exposing obvious and hidden abnormalities  
  • Real-time analysis of data which immediately warns of anomalies in data pattern by issuing signals  
  • Extensive and detailed analysis of data in its most microscopic form ensuing gap-free monitoring  
  • Higher accuracy in risk detection to avoid false alarms
  • Self-learning in systems through AI-powered algorithms that function quicker and better independently   


In machines, the learning process happens through stages, some automatic and some assisted manually. First, the system is fed with datasets which it studies to build a data model. Every time transactions happen, they are compared with the model. Any transaction that does not adhere to the model is considered a possible anomaly. A domain expert will approve of the anomaly, and the system will learn from this and continue working to improve its data model.  

Anomaly detection can be carried out through Supervised Machine Learning, Unsupervised Machine Learning, and Semi-supervised Machine Learning. 

Supervised Machine Learning

Under this method, the datasets are labeled. There are accompanying sets of normal samples and abnormal samples based on which data models are built by the machine. Algorithms learn on these models and help it to respond to new data using its knowledge of the existing data. Most machine learning is supervised learning. 

Regression and Classification are the two major types of supervised learning. Under regression, the data is matched, whereas classification segregates the data. Supervised learning is majorly applied in Bioinformatics and Database marketing. 

Unsupervised Machine Learning   

Here datasets are not labeled; the algorithms work on their own without reference to known or labeled inputs or outputs unlike in supervised learning. The underlying nature of the data itself is studied to come up with correlations and patterns like humans learn to form conclusions when faced with some information. Deep learning and neural network techniques find applications here. Clustering and Association are two types of unsupervised learning.  

Semi-supervised Machine Learning

Semi-supervised machine learning forms the middle ground between the above two. Here labeled data works in conjunction with a large volume of unlabeled data. Speech analysis and Web page classification are examples of where this method finds application. 

Benefits of using AI for anomaly detection with examples 

AI-assisted anomaly detection is changing the way most industries work for the better. Through the insights it provides, businesses save time, money and reputation, and run smoother operations.  

Many sectors are using AI-based anomaly detection. A few examples are  

Conclusion

Anomaly detection is a significant function of Machine Learning. The field poses many challenges due to the nature and volume of in-flowing data. The advancements in AI and ML can be used to increase the scope of anomaly harness its potential to increase value to the business.

Subscribe to our newsletter

Telefonica case study

Here’s how we help Telefónica use AI for anomaly detection

Get a free copy of the case study.

Related articles

AI

Responsible AI

Responsible AI is a framework that registers how an organization is addressing the challenges around artificial intelligence (AI) from an ethical and legal perspective.

Read More »

AI platform for the world’s
data-driven companies