DDOS-attacks detection using an efficient measurement-based statistical mechanism
Type
ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionEnvironmental Statistics Group
Statistics Program
Date
2020-06-09Online Publication Date
2020-06-09Print Publication Date
2020-08Submitted Date
2019-06-16Permanent link to this record
http://hdl.handle.net/10754/663578
Metadata
Show full item recordAbstract
A monitoring mechanism is vital for detecting malicious attacks against cyber systems. Detecting denial of service (DOS) and distributed DOS (DDOS) is one of the most important security challenges facing network technologies. This paper introduces a reliable detection mechanism based on the continuous ranked probability score (CRPS) statistical metric and exponentially smoothing (ES) scheme for enabling efficient detection of DOS and DDOS attacks. In this regard, the CRPS is used to quantify the dissimilarity between a new observation and the distribution of normal traffic. The ES scheme, which is sensitive in detecting small changes, is applied to CRPS measurements for anomaly detection. Moreover, in CRPS-ES approach, a nonparametric decision threshold computed via kernel density estimation is used to suitably detect anomalies. Tests on three publically available datasets proclaim the efficiency of the proposed mechanism in detecting cyber-attacks.Citation
Bouyeddou, B., Kadri, B., Harrou, F., & Sun, Y. (2020). DDOS-attacks detection using an efficient measurement-based statistical mechanism. Engineering Science and Technology, an International Journal. doi:10.1016/j.jestch.2020.05.002Sponsors
The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR-2019- CRG7-3800.Publisher
Elsevier BVAdditional Links
https://linkinghub.elsevier.com/retrieve/pii/S2215098619313023ae974a485f413a2113503eed53cd6c53
10.1016/j.jestch.2020.05.002
Scopus Count
Except where otherwise noted, this item's license is described as This is an open access article under the CC BY-NC-ND license.