Performance of Machine Learning and Big Data Analytics Paradigms in Cyber-security and Cloud Computing Platforms

Gabriel Kabanda

2021

Abstract

The purpose of the research is to evaluate Machine Learning and Big Data Analytics paradigms for use in Cybersecurity. Cybersecurity refers to a combination of technologies, processes and operations that are framed to protect information systems, computers, devices, programs, data and networks from internal or external threats, harm, damage, attacks or unauthorized access. The main characteristic of Machine Learning (ML) is the automatic data analysis of large data sets and production of models for the general relationships found among data. ML algorithms, as part of Artificial Intelligence, can be clustered into supervised, unsupervised, semi-supervised, and reinforcement learning algorithms. The Pragmatism paradigm, which is in congruence with the Mixed Method Research (MMR), was used as the research philosophy in this research as it epitomizes the congruity between knowledge and action. The researcher analysed the ideal data analytics model for cybersecurity which consists of three major components which are Big Data, analytics, and insights. The information that was evaluated in Big Data Analytics includes a mixer of unstructured and semi-structured data including social media content, mobile phone records, web server logs, and internet click stream data. Performance of Support Vector Machines, Artificial Neural Network, K-Nearest Neighbour, Naive-Bayes and Decision Tree Algorithms was discussed. To avoid denial of service attacks, an intrusion detection system (IDS) determined if an intrusion has occurred, and so monitored computer systems and networks, and then raised an alert when necessary. A Cloud computing setting was added which has advanced big data analytics models and advanced detection and prediction algorithms to strengthen the cybersecurity system. The research results presented two models for adopting data analytics models to cybersecurity. The first experimental or prototype model involved the design, and implementation of a prototype by an institution and the second model involved the use service provided by cloud computing companies. The researcher also demonstrated how this study addressed the performance issues for Big Data Analytics and ML, and its impact on cloud computing platforms.

Paper Citation

in Harvard Style

Kabanda G. (2021). Performance of Machine Learning and Big Data Analytics Paradigms in Cyber-security and Cloud Computing Platforms. In Proceedings of the 1st International Conference on Innovation in Computer and Information Science - Volume 1: ICICIS, ISBN 978-989-758-577-7, pages 33-50. DOI: 10.5220/0010789900003167

in Bibtex Style

@conference{icicis21,
author={Gabriel Kabanda},
title={Performance of Machine Learning and Big Data Analytics Paradigms in Cyber-security and Cloud Computing Platforms},
booktitle={Proceedings of the 1st International Conference on Innovation in Computer and Information Science - Volume 1: ICICIS,},
year={2021},
pages={33-50},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010789900003167},
isbn={978-989-758-577-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Innovation in Computer and Information Science - Volume 1: ICICIS,
TI - Performance of Machine Learning and Big Data Analytics Paradigms in Cyber-security and Cloud Computing Platforms
SN - 978-989-758-577-7
AU - Kabanda G.
PY - 2021
SP - 33
EP - 50
DO - 10.5220/0010789900003167