AI based Anomaly Detection

By: Arya Brijith, International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan,sia University, Taiwan,


In this article we shall discuss about anomaly detection and how AI can enhance it. We will also discover about 3 fields where AI based anomaly detection can be implemented, namely- cellular network, electric vehicles and IOT and sensors.


The critical role artificial intelligence (AI) plays in anomaly detection in a variety of businesses is examined in this article. Unusual occurrences in data streams, or anomalies, might provide important information. AI is useful for quickly detecting abnormalities because of its sophisticated algorithms and flexibility, especially in real-time applications.

Let us learn more about AI in anomaly detection.

What is an anomaly?

The goal of anomaly detection is to locate unusual or unusual events—also known as anomalous events—in data streams. Finding abnormalities in the data may provide fresh insights or be immediately beneficial in and of itself. [1]

Finding anomalies is important in a variety of industries, including manufacturing, healthcare, cybersecurity, and finance. These abnormalities can offer important new information or function as warning signs for future issues.

How can AI help in anomaly detection?

Artificial Intelligence revolutionizes anomaly detection by leveraging advanced algorithms and computational power to scrutinize vast datasets. AI models can discern regular patterns within data, enabling them to swiftly identify deviations that may signify anomalies. This ability is particularly crucial in real-time applications like cybersecurity, where immediate detection of irregularities can thwart potentially threats. Moreover, AI systems are adept at adapting to evolving circumstances, ensuring their effectiveness in environments where anomalies may manifest in diverse and dynamic ways.

AI significantly improves anomaly detection through process automation, increased precision, and real-time data analysis of large volumes. This has significant ramifications for sectors of the economy where it is critical to promptly identify irregularities.

Fields where AI based anomaly detection can be implemented

  • Cellular networks: AI-driven anomaly detection can be extremely important for cellular network traffic management optimization. It may identify abrupt increases in consumption or unexpected losses in signal quality by analysing trends in user behaviour. This enables proactive changes to be made to the distribution of network resources. Furthermore, cellular networks are vulnerable to a range of cyberattacks. In order to facilitate quick reaction and threat mitigation, artificial intelligence (AI) may monitor network traffic for odd patterns that can point to a security breach or a possible assault.
  • Electric vehicles: The battery is an essential part of electric cars. Artificial Intelligence has the capability to examine sensor data and detect abnormalities that may indicate possible problems with the battery, such damaged cells or overheating. Improved vehicle safety and prompt maintenance may result from this. In electric cars, anomaly detection can go beyond the battery. By keeping an eye on many car systems, artificial intelligence (AI) can forecast when parts are likely to break, enabling preventative maintenance to save expensive breakdowns.
  • IoT and sensors: AI-based anomaly detection in IoT systems can minimize energy use and pinpoint malfunctioning sensors or equipment. For example, AI can quickly identify abnormalities and initiate maintenance if a sensor starts to produce inconsistent data. IoT sensors are widely utilized to gather environmental data. AI can recognize anomalous patterns in this data, including temperature anomalies or spikes in pollutant levels, which may point to possible environmental problems or device faults in the monitoring system.


The incorporation of AI to detect anomalies is a significant development that will boost efficiency and security in several sectors. By using AI, industries can improve operational efficiency, secure their resistance to unforeseen disruptions, and set the standard for innovation and adaptation.


  1. Patrick Schneider, Fatos Xhafa, in Anomaly Detection and Complex Event Processing over IoT Data Streams, 2022
  2. DeMedeiros, K., Hendawi, A., & Alvarez, M. (2023). A survey of AI-based anomaly detection in IoT and sensor networks. Sensors23(3), 1352.
  3. Dixit, P., Bhattacharya, P., Tanwar, S., & Gupta, R. (2022). Anomaly detection in autonomous electric vehicles using AI techniques: A comprehensive survey. Expert Systems39(5), e12754.
  4. Hussain, B., Du, Q., Imran, A., & Imran, M. A. (2019). Artificial intelligence-powered mobile edge computing-based anomaly detection in cellular networks. IEEE Transactions on Industrial Informatics16(8), 4986-4996.
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  7. Singh, A., & Gupta, B. B. (2022). Distributed denial-of-service (DDoS) attacks and defense mechanisms in various web-enabled computing platforms: issues, challenges, and future research directions. International Journal on Semantic Web and Information Systems (IJSWIS)18(1), 1-43.
  8. Almomani, A., Alauthman, M., Shatnawi, M. T., Alweshah, M., Alrosan, A., Alomoush, W., & Gupta, B. B. (2022). Phishing website detection with semantic features based on machine learning classifiers: a comparative study. International Journal on Semantic Web and Information Systems (IJSWIS)18(1), 1-24.

Cite As

Brijith A. (2023) AI based Anomaly Detection, Insights2Techinfo, pp.1

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