• Register
  • Login

European Journal of Molecular & Clinical Medicine

  • Home
  • Browse
    • Current Issue
    • By Issue
    • By Subject
    • Keyword Index
    • Author Index
    • Indexing Databases XML
  • Journal Info
    • About Journal
    • Aims and Scope
    • Editorial Board
    • Publication Ethics
    • Indexing and Abstracting
    • Peer Review Process
    • News
  • Guide for Authors
  • Submit Manuscript
  • Contact Us
Advanced Search

Notice

As part of Open Journals’ initiatives, we create website for scholarly open access journals. If you are responsible for this journal and would like to know more about how to use the editorial system, please visit our website at https://ejournalplus.com or
send us an email to info@ejournalplus.com

We will contact you soon

  1. Home
  2. Volume 8, Issue 2
  3. Authors

Online ISSN: 2515-8260

Volume8, Issue2

Analyzing and Predicting Cyber Security Violations using Machine Learning Techniques

    Veeramakali T G. Swapna P Ila Chandana Kumari V N L N Murthy

European Journal of Molecular & Clinical Medicine, 2021, Volume 8, Issue 2, Pages 659-661

  • Show Article
  • Download
  • Cite
  • Statistics
  • Share

Abstract

To deepen our insight into the evolution of a threat situation, study of cyber incident data sources is an essential process. This is a relatively recent subject for science and many experiments still have to be conducted. Throughout this article, we present statistical analysis of the 12-year cyber hacking operation (2005-2017) violation incident data set which includes attacks bymalware. We prove that, in comparison to the literary results, breach sizes and inter-arrival times for hacking breaches can be modeled instead of distributions, since they have an auto-correlation. In order to adapt the time of the intercom and the scale of the violation, we suggest complex
stochastic process models. We also prove that the inter arrival periods and the violation scale can be estimated from these models. We perform quantitative and qualitative pattern research on the data set to achieve a better understanding of the growth of hacking infringement incidents. We derive a variety of observations into cyber security, including the challenge of cyber hacking in its scale, but not in its severity.
Keywords:
    Cyber risk analysis Hacking breach breach prediction data breach cyber threats trend analysis cyber security data analytics and time series
  • PDF (467 K)
  • XML
(2021). Analyzing and Predicting Cyber Security Violations using Machine Learning Techniques. European Journal of Molecular & Clinical Medicine, 8(2), 659-661.
Veeramakali T; G. Swapna; P Ila Chandana Kumari; V N L N Murthy. "Analyzing and Predicting Cyber Security Violations using Machine Learning Techniques". European Journal of Molecular & Clinical Medicine, 8, 2, 2021, 659-661.
(2021). 'Analyzing and Predicting Cyber Security Violations using Machine Learning Techniques', European Journal of Molecular & Clinical Medicine, 8(2), pp. 659-661.
Analyzing and Predicting Cyber Security Violations using Machine Learning Techniques. European Journal of Molecular & Clinical Medicine, 2021; 8(2): 659-661.
  • RIS
  • EndNote
  • BibTeX
  • APA
  • MLA
  • Harvard
  • Vancouver
  • Article View: 394
  • PDF Download: 570
  • LinkedIn
  • Twitter
  • Facebook
  • Google
  • Telegram
Journal Information

Publisher:

Email:  editor.ejmcm21@gmail.com

  • Home
  • Glossary
  • News
  • Aims and Scope
  • Privacy Policy
  • Sitemap

 

For Special Issue Proposal : editor.ejmcm21@gmail.com

This journal is licensed under a Creative Commons Attribution 4.0 International (CC-BY 4.0)

Powered by eJournalPlus