Since we, Software Planet Group company, first wrote about anomaly detection (AD) back in 2019, things have come a long way. In this article, we’re going to look at what anomaly detection was, as well as what it’s evolved into. We’ll also explore how it can be applied to benefit you.
What is Anomaly Detection?
There’s value in data, and companies know this. For years, they’ve been gathering Big Data for analysis, but they’ve come to realise that, without the proper analytical tools, it’s largely worthless. In order to extract the value from the data, a number of processes are necessary. One of these is anomaly detection.
When assessing massive amounts of data, it can become difficult to detect irregularities and anomalous, unhelpful data points. Anomaly detection employs algorithms to weed out these anomalies and improve the overall quality of the data set.
Traditionally, there have been two main models of anomaly detection algorithms and approaches, including principal component analysis (PCA) and artificial neural networks (ANN), to name just two.
Uses of Anomaly Detection
Beyond simply enhancing the statistical power of a set of data, anomaly detection has many real world applications. Here are a few examples:
Suspicious Activity
Anomalies in data can often represent users trying to bend or break the rules, and this may even constitute fraud. Anomaly detection can therefore be used to single these cases out, so that they can be addressed appropriately.
Marketing
Anomaly detection has many applications to marketing. It can be used to optimise advertisements, identify areas of a campaign which are underperforming, and help to explain patterns in consumer behaviour.
Efficiency Leak Detection
Anomaly detection has been used by companies to identify areas of unexplained loss. For example, Microsoft used an AD algorithm to detect water leakages in Norwegian facilities, ultimately reducing water usage by 30% and resulting in significant cost savings.
Intrusion Detection
Anomaly detection algorithms can examine a network to determine what typical behaviour looks like, then identify any outliers or abnormal traffic patterns. These can then be analysed to determine whether they indicate an intrusion.
Medical Diagnosis
In medical fields, AD can be used to identify abnormal results in medical data, including identifying tumours in MRI scans. Applying modern AD to endeavours such as this can result in a genuine reduction in loss of life, illustrating clearly just how powerful anomaly detection can be.
Evolution of Modern AD
In just five short years, the process of anomaly detection has changed substantially. Now we’re in the age of AI and machine learning, and tasks like anomaly detection are often more automated.
Advanced Models
PCA and ADD have largely been superseded by techniques such as Transformer architectures and autoencoder neural networks. These modern models allow for greater levels of efficiency and accuracy. With deep learning now in the mix, more complex levels of pattern recognition – and therefore anomaly detection – can be achieved.
Real-time Detection
The increases in the efficiency of AD, and the incorporation of edge computing, now allow for some forms of real-time data processing. Through this, it’s possible to detect anomalies as soon as they appear. Faster responses result in higher success rates in areas such as fraud protection.
Enhanced Reliability
With the improved technology underpinning modern anomaly detection comes better reliability. Explainable AI can explain why a particular anomaly has been flagged, making AD systems more reliable all-round.
Automatic Data Labelling
A few years ago, anomaly detection struggled with the processing of labeled data, requiring a significant amount of human intervention. However, it has since developed the ability to handle such tasks in a self-sufficient manner.
Less Emphasis on Structured Data
It’s no longer as important that data is properly structured. Contemporary AD is more than capable of detecting anomalies in unstructured data such as images and videos. This massively expands the range of applications AD has.
Digital Privacy
In the past few years, data privacy requirements have tightened significantly. Anomaly detection can be used to enforce these, and to ensure that compliance regulations are met.
Summary
Anomaly detection has become an invaluable tool that is both powerful and versatile when used effectively. Here at SPG we use AD in a variety of ways, integrated with AI, Big Data, and other cutting-edge technologies to maximise the value of your existing data sets.
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