Harnessing the Power of Machine Learning with Anomaly Detection

anomaly detection machine learning

The mention of Industry 4.0 brings to our mind some buzzwords that promise to revolutionize entire systems and usher in a new era. Among these buzzwords is machine learning, a subset of artificial intelligence that allows computers to learn from data sets and get better with time. The idea behind this is to find ways to use the vast amounts of data that companies have at their disposal to enhance efficiency and productivity. One major practical application of the technology is in a field known as anomaly detection.

What is Anomaly Detection?

Anomaly detection denotes the discovery of a rare exception to a given rule in terms of data patterns. Basically, it refers to the identification of data points that differ significantly from a majority of other data in a given set. These data points raise suspicions as they do not conform to the pattern expected of a group of data. What makes them particularly interesting is the fact that they often signify rare yet noteworthy events such as fraud or cyber intrusions.

To illustrate, let’s assume you have a neighbor who takes her dog out for a walk every afternoon at 2pm. For more than a year of living next door to her, she has never once failed to take her dog out for the afternoon walk. If one day she does not come out at that time, you could have reason to conclude that something out of the ordinary has happened. That exception would qualify as an anomaly, raising questions that could prompt further research.

Why Do Companies Need Anomaly Detection?

Thanks to the advent of the internet, there is more data available to companies now more than ever before. But with the increase in data, there has also been an increase in security threats to business such as cyber attacks.

In simple, straightforward situations, it is possible to separate anomalous data from normal data by use of data visualization. However, as one scales up to higher numbers of variables, the exercise becomes more and more complicated. Manual thresholds in such cases do not offer a viable, scalable solution to anomaly detection. This is where anomaly detection algorithms in machine learning come in.

Machine learning helps companies manage the vast amounts of data at their disposal as well as analyze transactions in real time. By identifying differences between data points, anomaly detection opens up interesting opportunities for companies. On one hand, it minimizes potential risks for business operators while on the other, it maximizes revenue potential. Moreover, it can help companies adapt to changing conditions rapidly.

How Anomaly Detection Algorithms in Machine Learning Work

Machine learning algorithms for anomaly detection process data points one at a time. As such, it constantly defines and redefines ‘business as usual’ using statistical tests to check available data. During processing, a number of events could take place:

  • The system creates a model based on data patterns.
  • Using this model, it predicts the value expected from the next data point in the sequence.
  • If there is a significant difference between the prediction and actual data point, the data point gets flagged as a potential anomaly
  • As the system flags potential anomalies, its algorithm digs deeper to establish relationships between available metrics. Using this information, it filters results down to a smaller number of actual anomalies.

Techniques of Anomaly Detection

Machine learning algorithms for anomaly detection make use of techniques that offer an efficient alternative to traditional approaches. Let us examine two main techniques:

Supervised Machine Learning Anomaly Detection

To use this approach, you need a labeled training data set containing both normal and anomalous samples. This set will facilitate the creation of a predictive model. In theory, supervised approaches are said to offer better performance than unsupervised models. Some of the popular models include:

  • K-nearest neighbor (k-NN)
  • Support vector machine learning
  • Decision trees
  • Bayesian networks

Unsupervised Machine Learning Anomaly Detection

Unlike the previous approach, the unsupervised model does not require data for training. Rather, its design is based on two main assumptions. For starters, it assumes that a majority of the data is normal and only a small percentage is anomalous. And second, it assumes that malicious traffic will bear statistical differences from normal traffic.

On the basis of these assumptions, it classifies the groups of data that occur more frequently as normal and the rest as abnormal. Some of the popular models used for this approach include:

  • K-means
  • Self-organizing maps
  • C-means
  • Adaptive resonance theory
  • Expectation-maximization meta-algorithm

Practical Applications of Anomaly Detection

  • Network Intrusion Detection

Anomaly detection is used to identify network intrusions as well as misuse. It does this by monitoring activity on a given system and classifying it as normal or anomalous. By flagging deviations from the norm as malicious activity, it allows for the creation of a defense line.

  • Monitoring Machine Health

It can also be used to keep track of machine health and send notifications when the behavior of components deviates from the norm. For highly interconnected production systems, it is especially hard to get information about machine status. But using this automated approach gives the user hints to help in identifying faults. This approach can effectively support predictive maintenance by sending information of abnormal activity that precedes component failure.

  • Fraud Detection

Fraudsters are ever adapting their techniques to remain a step ahead of security experts. As such, traditional fraud detection models are often reactive rather than predictive. But machine learning applications can capture common fraud patterns as well as new ones. This often uses unsupervised approaches, flagging potential fraudsters. In turn, it allows fraud busters to train their focus on these high-risk scenarios rather than carrying out random checks.

Benefits of Using Machine Learning Applications for Anomaly Detection

The use of machine learning applications for anomaly detection offers countless benefits to companies. Consider some of their top benefits for business:

  • Real-Time Insights

Having the backend process of anomaly detection automated using machine learning algorithms means getting access to insights in real time. With this information, it becomes possible to address anomalies immediately. And for situations that do not require an immediate response, you get the chance to prioritize your next steps.

  • Accuracy

Traditional anomaly detection processes involve a significant amount of guesswork, which is not always correct. But thanks to the use of machine learning models, one gets accurate insights using a less complex process. Business users thus get access to opportunities that are far beyond human capabilities.

  • Scalable Solution

It is highly unlikely that data generation would slow down in the foreseeable future. If anything, it can only keep increasing. Using automated algorithms is the only viable way for a business to handle the infinite number of data points accessed. And they are important tools that equip entrepreneurs with what they need to keep pace with the demands of business.

  • Full Business Automation

Armed with the necessary tools for automated anomaly detection, a business user will not only speed up response times. They will also have the opportunity to analyze the business as a whole. With an advanced system you can go deeper, analyzing relationships between patterns that span across the organization’s functions. With this, one can get the deep insights needed to optimize performance.

Fostering a Proactive Business Approach with Machine Learning Anomaly Detection

The use of machine learning in anomaly detection holds vast potential for business operators. Every entrepreneur could benefit from getting real-time insights on abnormal activities so as to act accordingly to avoid risks and optimize benefits. Having such insights on hand would also enhance the efficiency of your digital initiatives which are otherwise prone to cybersecurity risks. A combination of the above benefits in particular would serve to foster a more proactive approach in enterprise and gradually increase efficiency over time. Notably, it offers a solution that scales with your business to offer optimal performance at all stages of growth.

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Janica San Juan

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