A comprehensive survey of anomaly detection in banking, wireless sensor networks, social networks, and healthcare

Author:

Zamini Mohamad1,Hasheminejad Seyed Mohammad Hossein2

Affiliation:

1. Department of Information Technology, Tarbiat Modares University, Tehran, Iran

2. Department of Computer Engineering, Alzahra University, Tehran, Iran

Publisher

IOS Press

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Human-Computer Interaction,Software

Reference278 articles.

1. Hawkins DM. Identification of outliers. Springer. 1980; 11.

2. Pal K, Verma JS. A survey on anomaly based malware detection and demolition in false alarm rate. 2015.

3. Viji D, Banu SKZ. An improved credit card fraud detection using k-means clustering algorithm. in: International Journal of Engineering Science Invention (IJESI), One Day National Conference on “Internet of Things the Current Trend in Connected World” NCIOT. 2018; 59-64.

4. Tran PH, Tran KP, Huong TT, Heuchenne C, HienTran P, Le TMH. Real time data-driven approaches for credit card fraud detection. in: Proceedings of the 2018 International Conference on E-Business and Applications. ACM. 2018; 6-9.

5. Anomaly detection: A survey;Chandola;ACM Computing Surveys (CSUR),2009

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