Anomaly detection may seem like a buzzword, but it has many business benefits. Before now, analytics teams faced varying levels of issues, ranging from threats to simple inefficiencies.
Anomaly detection helps mitigate both alongside many other applications. It’s just like checking around the home before going to bed to make sure you’ve locked all doors to keep intruders out.
Another example, is the monthly maintenance on your car to check for faults and fine-tune all nuts.
It is also a lot more similar to our human brains and often tries to recognize what’s not ‘usual,’ ‘normal, or ‘abnormal.’
Recent data science research also shows that machine learning is critical for anomaly detection.
This brings about the question – how do they really fit? Where does anomaly detection begin and machine learning start? How does it affect your business?
This article talks about machine learning enhances the quality, speed, and effectiveness of detection.
What is Anomaly Detection?
Like our human brains, anomaly detection is primarily a technique for identifying irregular patterns, observations, or events. These events can be caused by suspicious activity.
Anomaly detection, also known as outlier detection, is the mode of identifying and detecting anomalous data in any data-based observation or event. Such anomalous data will be statistically different from the rest of the events.
Some real-world anomaly detection use cases include credit card fraud, data cleaning, cyber-attack, systems health monitoring, event detection in sensor networks, or malfunctioning equipment.
Anomalous data involves more than merely searching for it. So, you may wonder how to go about detecting an anomaly in data. Let’s look at an example.
Imagine we have an anomaly detection use case with two variables within our data. We can call these variables X & Y. We may have plots like:
On the right, we can clearly see data patterns of X and Y individually. But it is impossible to detect and identify the outliers. However, when we plot both variables shown on the left, one against the other helps us clearly detect the anomaly or outlier.
This brings us to the definition of an outlier. An outlier and anomaly are synonymous, but while anomaly is more of an umbrella term, outliers are better suited to the data plots.
Therefore, we can better define an outlier as any data point or object that significantly deviates from the remaining data points.
The Challenge Of Anomaly Detection
Now we have an idea about anomaly detection, it becomes clear that it would be a tedious process plotting hundreds of variables of datasets, which is what happens in real-life scenarios.
Building anomaly detection (AD) systems by hand require domain knowledge and an even more challenging aspect – foresight.
For instance, besides dealing with millions of variables of datasets, they also change over time. So building anomaly detection (AD) systems manually can never offer a solution. This is because every time the system fails, you will need more resources to improve the system. Yet, the lack of foresight will significantly reduce any progress made with each manual patch.
Therefore, manually building anomaly detection systems is an awful idea as the system fails to adapt or requires immense costs and time to adapt.
That’s where machine learning comes into the picture.
Machine Learning and Anomaly Detection
Machine learning originates as a sub-set of artificial intelligence (AI), responsible for automatically learning and expanding upon their experience without being overly programmed.
However, we also establish that machine learning algorithms can help us find anomalous data quickly. So machine learning in anomaly detection leads to:
- Classifying data
- Predicting the next upcoming value
- Detecting anomalies
- Uncovering structure
Through these four sub-sets of activities, machine learning offers a better analysis of data points in the dataset that do not generally behave like the rest of the data.
In this case, machine learning brings about immense benefits to the engineer as it can handle large data sets and is adaptive and on time.
But, when how do you picture performing anomaly detection using machine learning?
Let’s look at an example using the K-means clustering method.
K-means clustering is an unsupervised learning method ( we will explain this later). This method’s algorithm uses unlabeled data, which is data without defined groups, classes, or categories.
The K-means algorithm’s objective is to find particular K-defined non-overlapping sub-groups (clusters) in the data, where the number of groups in the data where each data point belongs to one group.
Therefore, it attempts to make intra-cluster data points as similar as possible while keeping the clusters far (different) apart. Consequently, it will allocate data points to a cluster in that it is the sum of the squared distance between data points and the cluster’s centroid (arithmetic mean of all data points in that specific cluster). When data points in the same cluster are more homogenous (similar), we can find fewer variations within that cluster.
Therefore K-means algorithm works as follows:
- Specifies the number of clusters K
- Starts creating centroids by initially shuffling data set to pick K data points for the centroids without replacements randomly
- Continues iterating until the assignment of data points to clusters are not changing
- Computes the sum of the squared distance between all centroids and data points
- Allocates each data point to the closed cluster (centroid)
- Computes the centroids for each cluster by assessing the average of all data points belonging to each cluster
- Therefore, the k-clustering algorithm method results are the centroids of the K clusters for labeling new data and the labels for each data point in the training data.
In a nutshell, the K-means clustering method is applied to identify outliers based on their plotted distance from the nearest cluster.
Applying Machine Learning in Anomaly Detection
K-means clustering is only one sub-technique for anomaly detection. In machine learning, we can have roughly four basic machine learning techniques:
- Semi-supervised machine learning
- Reinforcement machine learning
Supervised Machine Learning For Anomaly Detection
As the name implies, supervised anomaly detection methods require that we train the software with a labeled data set. This data will contain our pre-defined normal and anomalous data points. Then we test our created model against new sets of data to reach a prediction for them. Some examples of supervised machine learning techniques for anomaly detection include Bayesian networks, supervised neural networks, support vector machine learning, parameterization of the training model, and the K-nearest neighbors (k-NN) method.
Unsupervised Machine Learning For Anomaly Detection
Unsupervised learning for anomaly detection does not have a manually pre-defined label. In this case, we allow the model to work on its own to uncover information. Therefore, this begins on the statistical assumption that only a small percentage of the inflowing data would be anomalous while the remaining are normal.
So, these anomaly detection techniques would estimate that anomalous data would be statistically different from standard data. Here, the system will bring about the right outcome and explore data to provide inferences from datasets and describe hidden structures from unlabeled data. Some examples of such techniques include hypothesis-based analysis, clustering-based methods, self-organizing maps (SOM), adaptive resonance theory (ART), autoencoders, and C-means, K-means method.
This means using both labeled and unlabeled data to train the machine. It usually has a large amount of unlabeled data and a small amount of labeled data. Systems using semi-supervised machine learning algorithms can relatively enhance learning accuracy over time. Typically, semi-supervised is when acquired labeled data needs skilled and valuable resources to train and learn from it. Else, acquiring unlabeled data does not usually require such additional resources.
Reinforcement is a learning method based on interactions with the environment by developing actions and uncovering errors or rewards. Reinforcement learning often involves a trial and search followed by a delayed reward. With this method, machines and software agents can automatically assess ideal behavior in a particular context to maximize performance. The agent will receive simple reward feedback to learn which action is best, called reinforcement signal.
Benefits Of Anomaly Detection Using Machine Learning
Perhaps the most significant benefit of machine learning is the ability to analyze massive quantities of data and learn from it. However, in anomaly detection, these benefits go beyond analyzing millions of datasets and variables. Here are some other real business benefits that show that machine learning for anomaly detection leads to a greater impact.
1. Automated KPI Analysis
Most businesses handle KPI analysis by manually sorting through the data across their digital channel and dashboards. As the company continues collecting data, it becomes time-consuming and almost impossible to analyze data.
However, an anomaly detection system powered by machine learning will continuously check your data across all dashboards 24/7. When it finds anomalies and unusual behaviour, it instantly alerts you, thereby saving valuable time and effort. In turn, you can focus on the most critical task – finding out how these anomalies affect your business.
2. Prevention Of Threats And Security Breaches
Studies show that hacker attacks now take place every 39 seconds. Therefore, online security is now critical, and discovering such anomalies manually is a dangerous notion.
According to IBM, businesses take on average 206 days in 2019 to detect security threats, which is manual systems’ pitfall.
Think about just the amount of damage that that breath would cause before it becomes identified. Anomaly detection systems powered by AI helps us find those security breaches as soon as they occur.
3. Uncover Hidden Performance Opportunities
Although the word anomaly seems to mean bad data points, it isn’t always the case. Continuous analysis of your datasets can unearth hidden performance opportunities that would have gone on noticed. Even so, when AI-powered anomaly detection systems handle the repetitive task of analyzing datasets, your data analytics team can spend more time planning and executing high-impact performance driving strategies.
Paired with the prospects of hidden opportunities, you can drive your business bottom line by gaining the edge over your competitors.
4. Stretch Talents, Resources, And Budgets
Anomaly detection systems are not only available for the corporate giants. It can also help smaller businesses with tighter budgets, resources, and talents leverage what they have to bring realities to their ambitious projects.
Besides, your team will focus on innovation and creativity instead of analyzing KPIs for anomalies or putting out the numerous fires from missed anomalies.
That way, you can maximize the resources you have at your disposal while reaping incredible results.
Anomaly detection is a critical concept in the business world. Combined with the many machine learning techniques, it has begun yielding significant business values to businesses of all sizes. Your business no longer has to rely on instincts or trying to become a fortune teller.
You can now leverage your historical data to harness unlimited potentials for learning.
Therefore, your business should invest in the right anomaly detection tools to stay on top of your data, harness hidden opportunities without your analysts crunching data day in, day out.