Anomaly detection is a process in machine learning that identifies data points, events, and observations that deviates from a data set’s normal behavior.
One of the most popular applications of machine learning is anomaly detection. Outliers can be found and identified to stop fraud, adversary assaults, and network intrusions that could jeopardize the future of your business.
Anomaly detection can also help to point out where an error is occurring, enhancing root cause analysis and quickly getting tech support on the issue.
Applications of Anomaly detection:
· Cybersecurity: Network intrusion is a prominent example. One way an anomaly detection algorithm would do this would be monitoring traffic to establish normal levels and then identifying anything that falls outside this norm.
· Social media monitoring: To get a better understanding of user activity and engagement on social media as well as other forms of digital marketing and advertising, anomaly detection might identify that searches for a particular topic spike at certain times of the year, enabling advertisers and marketers to allocate their budgets accordingly.
· Machine performance: Digital twin technologies are a good example in this instance. A digital twin is an exact digital replica of a real-world machine, process, or piece of equipment. Anomaly detection can identify deviations in performance in the digital twin that are early warning signals of an impending failure in the real-world machine. This makes it possible to schedule maintenance of the machine before the failure occurs, reducing downtime and improving productivity.
· Medical monitoring: This is everything from identifying abnormal patterns or occurrences in an individual (such as an irregular heartbeat) to identifying health-related anomalies in groups of people such as the unusual spread of a disease over a short period of time in a particular geographical area.
Types of Anomalies:
· Point anomalies
· Collective anomalies
· Contextual anomalies
A point anomaly is where a single data point stands out from the expected pattern, range, or norm.
A collective anomaly appears occurs where single data points looked at in isolation appears normal. When you look at a group of these data points, however unexpected patterns, behaviors, or results become clear. An irregular heartbeat is an example of collective anomaly.
Instead of looking at specific data points or groups of data, an algorithm looking for contextual anomalies will be interested in unexpected results that come from what appears to be normal activity.
The crucial element here is context: Are the results out of context?
Supervised and unsupervised anomaly detection:
The main difference between supervised and unsupervised detection is the approach involved, where supervised approach makes use of predefined algorithms and AI training, while unsupervised approach uses a general outlier-detection mechanism based on pattern matching.
Anomaly detection in data analysis is an indispensable component of effective decision-making processes. By uncovering hidden irregularities, organizations can mitigate risks, optimize operations, and enhance overall system performance.
However, anomaly detection poses various challenges, from defining anomalies to handling noisy and complex data. By leveraging statistical methods, machine learning algorithms, time series analysis, and deep learning techniques, analyst can develop robust anomaly detection systems that adapt to the ever-evolving data landscape.
As organizations continue to harness the power of data, anomaly detection will remain a critical tool for unraveling valuable insights and ensuring data-driven success.