Welcome to the InfoSci Platform
On Cloud Data Transaction Security Using Encryption and Intrusion Detection

On Cloud Data Transaction Security Using Encryption and Intrusion Detection

Mahmoud Jazzar
Copyright: © 2017 |Volume: 19 |Issue: 4 |Pages: 9
ISSN: 1548-7717|EISSN: 1548-7725|EISBN13: 9781522511021|DOI: 10.4018/JCIT.2017100102
Cite Article Cite Article

MLA

Jazzar, Mahmoud. "On Cloud Data Transaction Security Using Encryption and Intrusion Detection." JCIT vol.19, no.4 2017: pp.13-21. http://doi.org/10.4018/JCIT.2017100102

APA

Jazzar, M. (2017). On Cloud Data Transaction Security Using Encryption and Intrusion Detection. Journal of Cases on Information Technology (JCIT), 19(4), 13-21. http://doi.org/10.4018/JCIT.2017100102

Chicago

Jazzar, Mahmoud. "On Cloud Data Transaction Security Using Encryption and Intrusion Detection," Journal of Cases on Information Technology (JCIT) 19, no.4: 13-21. http://doi.org/10.4018/JCIT.2017100102

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

The rapid increase of cybercrimes and wide-ranging security measures has created an obvious need for deep understanding of security vulnerabilities for Cloud Computing environments, and for best practices addressing such vulnerabilities. Cybercrime activities have affected many regional and international organizational functions and operations. Finding clear and direct evidence of cybercrimes is critical, because huge amounts of data are on networks, and the analysis of such data is complex. This paper propose and discuss a security-enhanced cloud data transaction model for simplifying and filtering cybercrime evidence. The model consumes a number of intrusion-detection sensor inputs that contribute to collecting and fine-tuning large items of evidence at a lower level. A relevant evidence-processing criteria are defined for further reduction and fine-tuning of cybercrime evidence. Initial results of the up-to-date testbed show that it is possible to reduce substantial levels of irrelevant patterns from randomly collected datasets.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.