SavedAdd your journal

Journals / Mugla Journal of Science and Technology / 2023 / Cilt: 9 Sayı: 1

THE ADJUSTED HISTOGRAM-BASED OUTLIER SCORE - AHBOS

Uğur BİNZAT
Engin YILDIZTEPE
Pages
92–100
Publication Date
2023-06-30
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results from a simulation study and real data application indicate that the adjusted histogram-based outlier score yields a better performance not only in the simulated data but also for various types of real data.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Histogram is a commonly used tool for visualizing data distribution.
📊
Key Findings & Data:The histogram-based outlier score is a fast unsupervised anomaly detection method that has become more popular because of the rapid increase in the amount of data collected in recent decades.
💡
Conclusion & Impact:Results from a simulation study and real data application indicate that the adjusted histogram-based outlier score yields a better performance not only in the simulated data but also for various types of real data.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:unsupervised anomaly detection · outlier · histogram

Abstract

Histogram is a commonly used tool for visualizing data distribution. It has also been used in semi-supervised and unsupervised anomaly detection tasks. The histogram-based outlier score is a fast unsupervised anomaly detection method that has become more popular because of the rapid increase in the amount of data collected in recent decades. Histogram-based outlier score can be computed using either static or dynamic bin-width histograms. When a histogram contains large gaps, the dynamic bin-width approach is preferred over the static bin-width approach. These gaps in a histogram usually occur as a result of various distributions in real data. When working with a static bin-width histogram, gaps can be utilized to acquire better distinction between outliers and inliers. In this study, we propose an adjusted version of the histogram-based outlier score named adjusted histogram-based outlier score, which considers neighboring bins prior to density estimation. Results from a simulation study and real data application indicate that the adjusted histogram-based outlier score yields a better performance not only in the simulated data but also for various types of real data.

🔖

Cite This Article

BİNZAT, U., YILDIZTEPE, E. (2023). THE ADJUSTED HISTOGRAM-BASED OUTLIER SCORE - AHBOS. Mugla Journal of Science and Technology, Cilt: 9 Sayı: 1, 92–100. https://ajindex.com/articles/950322

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (16)

  1. Chandola, V., Banerjee, A., and Kumar, V., “Anomaly Detection: a Survey”, ACM Computing Surveys (CSUR), 41(3), 1-58, 2009.
  2. Anscombe, F. J., “Rejection of Outliers”, Technometrics, 2(2), 123-146, 1960.
  3. Grubbs, F. E., “Procedures for Detecting Outlying Observations in Sample”, Technometrics, 11(1), 1-21, 1969.
  4. Hawkins, D. M., Identification of Outliers, London: Chapman and Hall, 1980.
  5. Breunig, M. M., Kriegel, H. P., Ng, R. T. and Sander, J., “LOF: Identifying Density Based Local Outlier”, In Proceedings of the 2000 ACM SIGMOD International Conference on Management of data, 2000, 93-104.
  6. Hodge, V. and Austin, J., “A survey of Outlier Detection methodologies”, Artificial Intelligence Review, 22, 85-126, 2004.
  7. Goldstein, M. and Uchida, S., “A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data”, PloS One, 11(4), 2016.
  8. Zoppi, T., Ceccarelli, A., Puccetti, T. and Bondavalli, A., “Which Algorithm Can Detect Unknown Attacks? Comparison of Supervised, Unsupervised and Meta-Learning Algorithms for Intrusion Detection”, Computers & Security, 127, 2023.
  9. Kind, A., Stoecklin, M. P. and Dimitropoulos, X., “Histogram-Based Traffic Anomaly Detection”, IEEE Transactions on Network and Service Management, 6(2), 110-121, 2009.
  10. Sabau, A. S., “Survey of Clustering Based Financial Fraud Detection Research”, Informatica Economica, 16(1), 110, 2012.
  11. Xie, M., Hu, J. and Tian, B., “Histogram-Based Online Anomaly Detection in Hierarchical Wireless Sensor Network”, In 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing And Communications, 2012. 751-759.
  12. Sharma, A., Pujari, A. K. and Paliwal, K. K., “Intrusion Detection Using Text Processing Techniques with a Kernel Based Similarity Measure”, Computers & Security, 26(7-8), 488-495, 2007.
  13. Carminati, M., Polino, M., Continella, A., Lanzi, A., Maggi, F. and Zanero, S., “Security Evaluation of a Banking Fraud Analysis System”, ACM Transactions on Privacy and Security (TOPS), 21(3), 1-31, 2018.
  14. Munir, M., Chattha, M. A., Dengel, A. and Ahmed, S., “A Comparative Analysis of Traditional and Deep Learning-Based Anomaly Detection Methods For Streaming Data”, In 2019 18th IEEE International Conference On Machine Learning and Applications (ICMLA), 2019, 561-566.
  15. Goldstein, M. and Dengel, A., “Histogram-Based Outlier Score (HBOS): A Fast Unsupervised Anomaly Detection Algorithm”, KI-2012: Poster and Demo Track, 59-63, 2012.
  16. Saba-Sadiya, S., Chantland, E., Alhanai, T., Liu, T. and