Article
Version 1
Preserved in Portico This version is not peer-reviewed
Applications of Data Mining Algorithms for Network Security
Version 1
: Received: 15 June 2019 / Approved: 16 June 2019 / Online: 16 June 2019 (02:42:59 CEST)
How to cite: Chain, K. Applications of Data Mining Algorithms for Network Security. Preprints 2019, 2019060144. https://doi.org/10.20944/preprints201906.0144.v1 Chain, K. Applications of Data Mining Algorithms for Network Security. Preprints 2019, 2019060144. https://doi.org/10.20944/preprints201906.0144.v1
Abstract
Typical modern information systems are required to process copious data. Conventional manual approaches can no longer effectively analyze such massive amounts of data, and thus humans resort to smart techniques and tools to complement human effort. Currently, network security events occur frequently, and generate abundant log and alert files. Processing such vast quantities of data particularly requires smart techniques. This study reviewed several crucial developments of existent data mining algorithms, including those that compile alerts generated by heterogeneous IDSs into scenarios and employ various HMMs to detect complex network attacks. Moreover, sequential pattern mining algorithms were examined to develop multi-step intrusion detection. These studies can focus on applying these algorithms in practical settings to effectively reduce the occurrence of false alerts. This article researched the application of data mining algorithms in network security. The academic community has recently generated numerous studies on this topic.
Keywords
data mining; network security; association rules; DDoS
Subject
Computer Science and Mathematics, Security Systems
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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