Data analytics for network intrusion detection. Issue 2 (2nd April 2020)

• Record Type: Journal Article
• Authors: Wang, Lidong; Jones, Randy
• Is Part Of: Journal of cyber security technology. Volume 4:Issue 2(2020)
• Page Start: 106
• Page End: 123
• Publication Date: 2020-04-02
• Subjects: Data analytics -- network intrusion -- deep learning -- hidden Markov model (HMM) -- Naïve Bayesian classification; Computer security -- Periodicals; Data encryption (Computer science) -- Periodicals; 005.805
• DOI: 10.1080/23742917.2019.1703525
• Languages: English
• ISSNs: 2374-2917

Abstract

ABSTRACT: A network intrusion can be any unauthorized activity on a network and network intrusion detection is a significant topic in cybersecurity. Data analytics is conducted on the database 'spambase' as an example of analysis for network intrusion detection based on the Naïve Bayesian classification, deep learning with the algorithm of Rprop + and the hidden Markov model (HMM), respectively. All the analysis is fulfilled using R language and its functions. An HMM based on the Baum–Welch algorithm has been created on the database 'spambase' through training and parameter estimation. An HMM-based spam-email prediction has been performed through the probability evaluation based on the forward algorithm. The analytics results obtained from the above three methods are compared. It is shown that HMM-based analytics can achieve the best accuracy in the spam-email classification although only a few features are used in the HMM while all features are used in the Naïve Bayesian classification and deep learning.