Performance of Machine Learning and Big Data Analytics paradigms in Cybersecurity and Cloud Computing Platforms

Performance of Machine Learning and Big Data Analytics paradigms in Cybersecurity and Cloud Computing Platforms

Professor Gabriel Kabanda

Contact

Performance of Machine Learning and Big Data Analytics paradigms in Cybersecurity and Cloud Computing Platforms

Article Fingerprint

ReserarchID

201FW

Performance of Machine Learning and Big Data Analytics paradigms in Cybersecurity and Cloud Computing Platforms Banner

AI TAKEAWAY

Connecting with the Eternal Ground

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.

References

51 Cites in Article

Reference Format

D Berman,A Buczak,J Chavis,C Corbett (2019). Survey of Deep Learning Methods for Cyber Security.
I Sarker,A Kayes,S Badsha,H Alqahtani,P Watters,A Ng (2020). Cybersecurity data science: an overview from machine learning perspective.
P Bringas,I Santos (2010). Bayesian Networks for Network Intrusion Detection, Bayesian Network.
M Bloice,A Holzinger (2018). A Tutorial on Machine Learning and Data Science Tools with Python.
(2018). Framework for Improving Critical Infrastructure Cybersecurity, Version 1.1.
Roy Nielsen (2015). CS651 Computer Systems Security Foundations 3d Imagination Cyber Security Management Plan.
K Umamaheswari,S Sujatha (2017). Impregnable Defence Architecture using Dynamic Correlation-based Graded Intrusion Detection System for Cloud.
K Napanda,H Shah,L Kurup (2015). Artificial Intelligence Techniques for Network Intrusion Detection.
E Proko,A Hyso,D Gjylapi (2018). DETECTING WEB ATTACKS USING MACHINE LEARNING ALGORITHMS.
T Truong,Q Diep,I Zelinka (2020). Artificial Intelligence in the Cyber Domain: Offense and Defense.
S Mazumdar,J Wang (2018). Guide to Vulnerability Analysis for Computer Networks and Systems.
Berlin Wilson,Babak Khazaei,Laurence Hirsch (2015). Enablers and Barriers of Cloud Adoption among Small and Medium Enterprises in Tamil Nadu.
Ibrahim Hashem,Ibrar Yaqoob,Nor Anuar,Salimah Mokhtar,Abdullah Gani,Samee Ullah Khan (2015). The rise of “big data” on cloud computing: Review and open research issues.
M Siti Nurul Mahfuzah,S Sazilah,B Norasiken (2017). An Analysis of Gamification Elements in Online Learning to Enhance Learning Engagement.
F Menzes,G Liska,M Cirillo,M Vivanco (2016). Data Classification with Binary Response through the Boosting Algorithm and Logistic Regression.
W Stallings (2015). Operating System Stability.
Raymond Cox,Null- Wang (2014). Predicting the US bank failure: A discriminant analysis.
Chaowei Yang,Manzhu Yu,Fei Hu,Yongyao Jiang,Yun Li (2017). Utilizing Cloud Computing to address big geospatial data challenges.
J Fernando,L Dawson (2009). The health information system security threat lifecycle: An informatics theory.
M Moorthy,R Baby,S Senthamaraiselvi (2014). An Analysis for Big Data and its Technologies.
R Lakshami (2019). Machine Learning for Cyber Security using Big Data Analytics Journal of Artificial Intelligence.
David Snowdon,Michelle Sargent,Cylie Williams,Stephen Maloney,Kirsten Caspers,Nicholas Taylor (2019). Effective clinical supervision of allied health professionals: a mixed methods study.
Haslinda Hassan (2017). Organisational factors affecting cloud computing adoption in small and medium enterprises (SMEs) in service sector.
E Bou-Harb,P Celeda (2018). Survey of Attack Projection, Prediction, and Forecasting in Cyber Security.
Pense (2014). Pesquisa Nacional de Saude do Escolar.
E Thomas,A Temko,W Marnane,G Boylan,G Lightbody (2013). Discriminative and Generative Classification Techniques Applied to Automated Neonatal Seizure Detection.
M Gercke (2012). Understanding Cybercrime Prevention.
Mrs Sumathymurugan (2014). Detecting Anomaly IDS in Network using Bayesian Network.
Damiano Bolzoni (2009). Revisiting anomaly-based network intrusion detection systems.
W Jiang,L Wang,H Lin (2016). The role of cognitive processes and individual differences in the relationship between abusive supervision and employee career satisfaction.
Lawrence Cunningham (2008). The SEC's Global Accounting Vision: A Realistic Appraisal of a Quixotic Quest.
C Pu,M Kitsuregawa (2019). Unknown Title.
A Suryavanshi (2017). Magnesium oxide nanoparticle-loaded polycaprolactone composite electrospun fiber scaffolds for bone-soft tissue engineering applications: in-vitro and in-vivo evaluation.
D Burt,P Nicholas,K Sullivan,T Scoles (2013). Gilmour, Sir (John) Nicholas, (born 15 Dec. 1970).
Aithal Pai (2017). CSR und Kleinstunternehmen.
Marzantowicz (2014). Corporate Social Responsibility of TSL sector: attitude analysis in the light of research.
Christoph Fehling,Frank Leymann,Ralph Retter,Walter Schupeck,Peter Arbitter (2014). Cloud Computing Patterns.
Kpmg (2018). Endgame: KPMG and Jenkens.
J Kobielus (2018). Deploying Big Data Analytics Applications to the Cloud: Roadmap for Success.
J Lee (2017). 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS).
Limao Zhang,Xianguo Wu,Miroslaw Skibniewski,Jingbing Zhong,Yujie Lu (2014). Bayesian-network-based safety risk analysis in construction projects.
F Iafrate (2015). From Big Data to Smart Data.
A Almutairi (2016). Improving intrusion detection systems using data mining techniques.
Anatolij Bezemskij,George Loukas,Diane Gan,Richard Anthony (2017). Detecting Cyber-Physical Threats in an Autonomous Robotic Vehicle Using Bayesian Networks.
J Karimpour,S Lotfi,A Siahmarzkooh (2016). Intrusion detection in network flows based on an optimized clustering criterion.
Z Stefanova (2018). Machine Learning Methods for Network Intrusion Detection and Intrusion Prevention Systems.
K Hammond (2015). Practical Artificial Intelligence for Dummies®, Narrative Science Edition.
Iffat Gheyas,Ali Abdallah (2016). Detection and prediction of insider threats to cyber security: a systematic literature review and meta-analysis.
Murat Kantarcioglu,Bowei Xi (2016). Adversarial Data Mining.
R Kumar (2011). Research Methodology: A step by step guide for beginners 3 rd.
C Kothari (2004). Research Methodology Methods and Techniques 2 nd Revised Edition.

Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Professor Gabriel Kabanda. 2021. \u201cPerformance of Machine Learning and Big Data Analytics paradigms in Cybersecurity and Cloud Computing Platforms\u201d. Global Journal of Computer Science and Technology - G: Interdisciplinary GJCST-G Volume 21 (GJCST Volume 21 Issue G2): .

More Citation Formats

Select Citation Style:

Download Citation

Download Article

GJCST Volume 21 Issue G2
Pg. 1- 25

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Not Found

Classification

GJCST-G Classification: K.4.4

Submission ReceivedSeptember 9, 2021
Peer Review Double Blind
Handling Editor
Accepted October 4, 2021
Published October 15, 2021

Version of record

v1.2

Issue date

August 20, 2021

Language

Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D

Article Matrices

Total Views: 3506

Total Downloads: 1042

2026 Trends

Published Article

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]