FAAD : a self-optimizing algorithm for anomaly detection
Joint Authors
al-Hashimi, Udail
Ahmad, Tanvir
Source
The International Arab Journal of Information Technology
Issue
Vol. 17, Issue 2 (31 Mar. 2020), pp.272-280, 9 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2020-03-31
Country of Publication
Jordan
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Abstract EN
Anomaly/Outlier detection is the process of finding abnormal data points in a dataset or data stream.
Most of the anomaly detection algorithms require setting of some parameters which significantly affect the performance of the algorithm.
These parameters are generally set by hit-and-trial; hence performance is compromised with default or random values.
In this paper, the authors propose a self-optimizing algorithm for anomaly detection based on firefly meta-heuristic, and named as Firefly Algorithm for Anomaly Detection (FAAD).
The proposed solution is a non-clustering unsupervised learning approach for anomaly detection.
The algorithm is implemented on Apache Spark for scalability and hence the solution can handle big data as well.
Experiments were conducted on various datasets, and the results show that the proposed solution is much accurate than the standard algorithms of anomaly detection.
American Psychological Association (APA)
al-Hashimi, Udail& Ahmad, Tanvir. 2020. FAAD : a self-optimizing algorithm for anomaly detection. The International Arab Journal of Information Technology،Vol. 17, no. 2, pp.272-280.
https://search.emarefa.net/detail/BIM-954685
Modern Language Association (MLA)
al-Hashimi, Udail& Ahmad, Tanvir. FAAD : a self-optimizing algorithm for anomaly detection. The International Arab Journal of Information Technology Vol. 17, no. 2 (Mar. 2020), pp.272-280.
https://search.emarefa.net/detail/BIM-954685
American Medical Association (AMA)
al-Hashimi, Udail& Ahmad, Tanvir. FAAD : a self-optimizing algorithm for anomaly detection. The International Arab Journal of Information Technology. 2020. Vol. 17, no. 2, pp.272-280.
https://search.emarefa.net/detail/BIM-954685
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 279-280
Record ID
BIM-954685