On Cloud Data Transaction Security Using Encryption and Intrusion Detection

On Cloud Data Transaction Security Using Encryption and Intrusion Detection

Mahmoud Jazzar (Department of IT, Royal University for Women, Riffa, Bahrain)
Copyright: © 2017 |Pages: 9
DOI: 10.4018/JCIT.2017100102

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.
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Background

According to The CERT Division of SEI (n. d.) and Jazzar (2013) “The best way for administrators to protect their networks is to monitor and analyze their network traffic. Understanding the traffic can help them characterize threats and attacks, and it can also help them identify vulnerabilities in their networks. However, processing traffic on large networks can be time-consuming and expensive, and it may be impossible without effective automation tools…” (p. 4). IDS is an effective network traffic analyzer and defense tool that can analyze and identify vulnerabilities, as well as detect intrusion, exploits, and hostile activities on the system network. This study attempts to support the current IDS by supplementing with an inference monitor system that works in unsupervised learning mode, which can provide adoption, integrity, and an information-sharing platform among the IDS components.

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