Krassie Petrova

Dr. Krassie Petrova

orcid

Research Focus & Metrics

Academic Career Length

20Years (2007-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 June 2016 in Computers & Education
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Journal Article
Closed access
HLHyunjeong Lee
Hyunjeong Lee
DPDavid Parsons
David Parsons
GKGyuhyun Kwon
Gyuhyun Kwon
JKJieun Kim
Jieun Kim
KPKrassie Petrova
Krassie Petrova
HRHokyoung Ryu
Hokyoung Ryu

3 January 2024 in International Journal of Information Security
Verified
Journal Article
Closed access
PDF file
NUNdukwe Ukeje
Ndukwe Ukeje
Jairo GutierrezJairo Gutierrez
Jairo Gutierrez
KPKrassie Petrova
Krassie Petrova

3 November 2010 in Electronic Commerce Research
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Journal Article
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KPKrassie Petrova
Krassie Petrova
BWBin Wang
Bin Wang

27 February 2022 in Journal of Computer Networks and Communications
Verified
Journal Article
Open access
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NONoah Oghenefego Ogwara
Noah Oghenefego Ogwara
KPKrassie Petrova
Krassie Petrova
MYMee Loong Yang
Mee Loong Yang
Attacks on cloud computing (CC) services and infrastructure have raised concerns about the efficacy of data protection mechanisms in this environment. The framework developed in this study (CCAID: cloud computing, attack, and intrusion detection) aims to improve the performance of intrusion detection systems (IDS) operating in CC environments. It deploys a proposed new hybrid ensemble feature selection (FS) method. The ensemble includes FS algorithms of three different types (filter, wrapper, and embedded algorithms). The selected features used to train the ML (machine learning) model of the intrusion detection component comprised a binary detection engine for the identification of malicious/attack packets and a multiclassification detection engine for the identification of the type of attack. Both detection engines deploy ensemble classifiers. Experiments were carried out using the NSL KDD dataset. The binary model achieved a classification accuracy of 99.55% with a very low false alarm rate of 0.45%. The classification accuracy of the multiclassification model was also high (98.92%). These results compare very favourably with the results reported in the literature and indicate the feasibility of the framework implementation.

1 October 2013 in Journal of Electronic Commerce in Organizations
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Journal Article
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KPKrassie Petrova
Krassie Petrova
BWBo Wang
Bo Wang
This qualitative study aimed to identify the motivating factors and the challenges related to the adoption of mobile payment (mPayment) by small business retailers. Data collected from semi-structured personal interviews with a small group of participants were analyzed applying a content analysis approach. The findings indicate that retailer demand for mPayment was motivated mainly by perceived customer expectations for a convenient (faster) way to pay using the ubiquitous mobile technology, as well as by the perceived efficiency of mPayment leading to revenue increase. Challenges to mPayment adoption included, among others, the need to compete with already established point-of-sale payment technologies and the lack of information about mPayment leading to uncertainty about its comparative advantages. The study contributes to the body of knowledge by developing and exploring a merchant oriented mPayment adoption model. The factors identified as adoption drivers and challenges provide an insight into New Zealand retailer perspectives on mPayment, and the grounds for recommendations to mPayment service providers.

1 January 2007 in International Journal of Innovation and Learning
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Journal Article
Closed access
KPKrassie Petrova
Krassie Petrova
The predominant mode of electronic learning today is 'online' (web-based). This type of learning relies on student access to personal computers ? both on and off campus. Mobile technologies offer other educational alternatives. This article defines mobile learning (mLearning) as a new paradigm of flexible learning and describes scenarios for blended learning, which integrates the mobile and the online learning environments. A research model for the study of mLearning adoption is proposed, and hypotheses are derived that allow the study of mLearning adoption from a pedagogical and from a business perspective and can be applied to a variety of learning scenarios.