A Consumer-Oriented Cloud Forensic Process Model

Moussa, Ahmed Nour; Ithnin, Norafidah; Almolhis, Nawaf; Zainal, Anazida

doi:10.1109/icsgrc.2019.8837096

Cited by 13 publications

(3 citation statements)

References 20 publications

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“…To expedite and create continuous learning practices, modern higher education institutions have generally adopted multiple different technologies. Among these, e-learning systems are the most common [4,5], and Cloud computing based e-learning in particular has experienced significant growth in the last decade [6][7][8]. However, according to the literature, the issue of student disengagement and boredom has arisen with regard to e-learning systems [9,10].…”

Section: Introductionmentioning

confidence: 99%

A Conceptual Engagement Framework for Gamified E-Learning Platform Activities

Alsubhi¹,

Sahari²,

Wook³

2020

Int. J. Emerg. Technol. Learn.

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University students utilise e-learning resources to gain easy access to information and to make general enquiries. Such online environments offer alternatives to traditional learning, which is now considered expensive, and time-consuming with varying results. Student disengagement is reported as the main problem for higher education e-learning platforms, however, leading e-learning developers to employ Gamification to counteract this issue. A plethora of game elements are used in the gamification of e-learning, however, that necessitates more careful consideration of the various learning activities used in e-learning platforms. This study thus identifies several game elements suitable for inclusion in learning activities in e-learning platforms to improve student engagement, based on a literature-based analysis. This identified the most commonly used game elements, and a subsequent questionnaire survey examined real user perspectives on these elements. As a result, a conceptual engagement framework outlining how game elements might be used to support e-learning activities to engage students is proposed. The framework is intended to be used as a guideline for developers and academics seeking to build engaging e-learning systems based on good foundational concepts. The paper thus presents a short review of existing e-learning gamification frameworks and the steps taken towards the identification of commonly used game elements. Learning activities and student engagement factors usually employed in e-learning platforms are subsequently discussed in relation to the development of the proposed conceptual engagement framework for gamified e-learning activities.

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Section: Introductionmentioning

confidence: 99%

A Conceptual Engagement Framework for Gamified E-Learning Platform Activities

Alsubhi¹,

Sahari²,

Wook³

2020

Int. J. Emerg. Technol. Learn.

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“…There are two ways of removing noise from collected sound data. That is with the use threshold based de-noising method [35,36,37] and recursive least squares adaptive filtering method [38,39]. However, the first is adopted in our work for its suitability of smart devices.…”

Section: Pre-processingmentioning

confidence: 99%

Untitled

2020

IJSPTM

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Behavioral biometric authentication is considered as a promising approach to securing the internet of things (IoT) ecosystem. In this paper, we investigated the need and suitability of employing voice recognition systems in the user authentication of the IoT. Tools and techniques used in accomplishing voice recognition systems are reviewed, and their appropriateness to the IoT environment are discussed. In the end, a voice recognition system is proposed for IoT ecosystem user authentication. The proposed system has two phases. The first being the enrollment phase consisting of a pre-processing step where the noise is removed from the voice for the enrollment process, the feature extraction step where feature traits are extracted from user's voice, and the model training step where the voice model is trained for the IoT user. And the second being the phase verifies whether the identity claimer is the owner of the IoT device. Based on the resources limitedness of the IoT technologies, the suitability of text-dependent voice recognition systems is promoted. Likewise, the use of MFCC features is considered in the proposed system.

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“…With the proliferation of cloud services, ensuring data security and privacy has become paramount. Enterprises, especially those dealing with sensitive information, face challenges in relinquishing control over their data when it resides in the cloud [7]. Concerns arise regarding data security in transit, processing, and storage.…”

Section: Introduction 1overviewmentioning

confidence: 99%

Cloud Security Using Fine-Grained Efficient Information Flow Tracking

Alqahtani,

Almutairi,

Sheldon

2024

Future Internet

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This study provides a comprehensive review and comparative analysis of existing Information Flow Tracking (IFT) tools which underscores the imperative for mitigating data leakage in complex cloud systems. Traditional methods impose significant overhead on Cloud Service Providers (CSPs) and management activities, prompting the exploration of alternatives such as IFT. By augmenting consumer data subsets with security tags and deploying a network of monitors, IFT facilitates the detection and prevention of data leaks among cloud tenants. The research here has focused on preventing misuse, such as the exfiltration and/or extrusion of sensitive data in the cloud as well as the role of anonymization. The CloudMonitor framework was envisioned and developed to study and design mechanisms for transparent and efficient IFT (eIFT). The framework enables the experimentation, analysis, and validation of innovative methods for providing greater control to cloud service consumers (CSCs) over their data. Moreover, eIFT enables enhanced visibility to assess data conveyances by third-party services toward avoiding security risks (e.g., data exfiltration). Our implementation and validation of the framework uses both a centralized and dynamic IFT approach to achieve these goals. We measured the balance between dynamism and granularity of the data being tracked versus efficiency. To establish a security and performance baseline for better defense in depth, this work focuses primarily on unique Dynamic IFT tracking capabilities using e.g., Infrastructure as a Service (IaaS). Consumers and service providers can negotiate specific security enforcement standards using our framework. Thus, this study orchestrates and assesses, using a series of real-world experiments, how distinct monitoring capabilities combine to provide a comparatively higher level of security. Input/output performance was evaluated for execution time and resource utilization using several experiments. The results show that the performance is unaffected by the magnitude of the input/output data that is tracked. In other words, as the volume of data increases, we notice that the execution time grows linearly. However, this increase occurs at a rate that is notably slower than what would be anticipated in a strictly proportional relationship. The system achieves an average CPU and memory consumption overhead profile of 8% and 37% while completing less than one second for all of the validation test runs. The results establish a performance efficiency baseline for a better measure and understanding of the cost of preserving confidentiality, integrity, and availability (CIA) for cloud Consumers and Providers (C&P). Consumers can scrutinize the benefits (i.e., security) and tradeoffs (memory usage, bandwidth, CPU usage, and throughput) and the cost of ensuring CIA can be established, monitored, and controlled. This work provides the primary use-cases, formula for enforcing the rules of data isolation, data tracking policy framework, and the basis for managing confidential data flow and data leak prevention using the CloudMonitor framework.

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A Consumer-Oriented Cloud Forensic Process Model

Cited by 13 publications

References 20 publications

A Conceptual Engagement Framework for Gamified E-Learning Platform Activities

A Conceptual Engagement Framework for Gamified E-Learning Platform Activities

Untitled

Cloud Security Using Fine-Grained Efficient Information Flow Tracking

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