Scalable Unsupervised Algorithms for Anomaly Detection in Field Monitoring Systems

References

[1] Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3), 1-58

[2] Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. ACM SIGMOD Record, 29(2), 93-104.

[3] Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, 226-231.

[4] Agarwal, A. V., Verma, N., & Kumar, S. (2018). Intelligent Decision Making Real-Time Automated System for Toll Payments. In Proceedings of International Conference on Recent Advancement on Computer and Communication: ICRAC 2017 (pp. 223-232). Springer Singapore.

[5] Zhou, C., & Paffenroth, R. C. (2017). Anomaly detection with robust deep autoencoders. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 665-674.

[6] Ahmed, M., Mahmood, A. N., & Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19-31.

[7] Agarwal, A. V., & Kumar, S. (2017, October). Intelligent multi-level mechanism of secure data handling of vehicular information for post-accident protocols. In 2017 2nd International Conference on Communication and Electronics Systems (ICCES) (pp. 902-906). IEEE.

[8] Schubert, E., Zimek, A., & Kriegel, H. P. (2012). Local outlier detection reconsidered: A generalized view on locality with applications to spatial, video, and network anomaly detection. Data Mining and Knowledge Discovery, 28(1), 190-237.

[9] Ruff, L., Kauffmann, J. R., Vandermeulen, R. A., et al. (2021). Unsupervised anomaly detection with deep learning: A review. Statistical Science, 36(4), 631-656

[10] Lazarevic, A., & Kumar, V. (2005). Feature bagging for outlier detection. Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, 157-166.

@article{1704393,
      author = {Newness Skymax},
      title = {Scalable Unsupervised Algorithms for Anomaly Detection in Field Monitoring Systems},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {11},
      pages = {906-909},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704393.pdf},
      abstract = {Anomaly detection is a crucial component of field monitoring systems, especially in scenarios where systems need to operate in real-time, continuously gathering data across vast environments. The complexity and scale of data in such systems demand efficient and scalable algorithms to detect irregularities or anomalies. Traditional anomaly detection methods often rely on supervised learning models, which require labeled data for training, posing challenges in real-world applications where labels are sparse or unavailable. Unsupervised learning techniques, however, do not require labeled data and have gained prominence for their ability to handle large-scale, complex datasets with minimal human intervention. This article explores the use of scalable unsupervised anomaly detection algorithms in field monitoring systems, discussing their advantages, challenges, and the state-of-the-art techniques employed in these systems. We analyze key algorithms such as clustering-based methods, distance-based methods, and neural network-based approaches, evaluating their applicability, scalability, and effectiveness in real-world applications. By examining recent advancements, this article highlights the future potential and emerging trends in unsupervised anomaly detection for field monitoring.},
      keywords = {Anomaly detection, unsupervised learning, field monitoring systems, scalable algorithms, clustering, distance-based methods, neural networks, real-time data, data analytics, machine learning.},
      month = {May},
  }