Cyber-Security: The Use of Big Data Analytic Model for Network Intrusion Detection Classification

Abstract

Cybersecurity is seen as a major player in the protection of Internet-connected systems, including hardware, software and data, from cyberattacks and other malicious crimes in today’s densely connected world-Internet of Things (IoTs). The divers challenge facing Internet users as private and business entities is being advocated as not enough hinderance to seamless interfacing of Mobile Computing and Internet Applications presently making waves. Technology such as Intrusion Detection Systems (IDS) application into cyber-security is an evolving computing mechanism designed as a counter-measure to incessant network threats and intruders. It is one of most reliable pro-defensive tools and has gained significance over time. Meanwhile network traffic data being generated within the context of enormous Internet users requires the application of big data analytical tools for its analysis. This paper, therefore, employs the use of big data analytical tools with its machine learning algorithm on an open-source data set-KDD’99. The full data set was used in the analysis. Predictive model was built in less than 5 minutes time with 99.91% prediction accuracy. Computational challenge and only 10% data set usage, which could only be accounted for in previous research were overcome. Therefore, IDS could be better designed with integration of this classification model result.

Keywords: Cyber-Security, Internet of Things, Intrusion, Mobile, Big data, network

DOI: 10.7176/CEIS/10-7-02

Publication date: November 30th 2019