Preprint Article Version 1 This version is not peer-reviewed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 (doi: 10.20944/preprints201906.0144.v1). Chain, K. Applications of Data Mining Algorithms for Network Security. Preprints 2019, 2019060144 (doi: 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.

Subject Areas

data mining; network security; association rules; DDoS

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