A Survey and a Credit Card Fraud Detection and Prevention Model using the Decision Tree Algorithm

Alraddadi, Abdulaziz Saleh

doi:10.48084/etasr.6128

Cited by 11 publications

(7 citation statements)

References 34 publications

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“…Generally, design science is known as a systematic and rigorous approach. It aids in creating models and solutions that incorporate modeling and analysis [9,60,61]. It is a powerful research methodology that combines theory and practice to create and evaluate innovative artifacts or systems [62].…”

Section: Methodsmentioning

confidence: 99%

A Cybersecurity Awareness Model for the Protection of Saudi Students from Social Media Attacks

Alotibi

2024

Eng. Technol. Appl. Sci. Res.

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Social engineering addresses a broad category of techniques aiming to persuade someone to reveal data or perform actions for criminal purposes, such as disclosing personal information about a particular target. Cybersecurity awareness is required to raise people’s understanding of how these social engineering techniques are being used and so their capacity to exploit them. To accomplish this objective, primary focus is given to educating and training individuals on how to recognize such incidents and respond to them effectively. To protect people against social engineering threats, various cybersecurity models and approaches have been proposed. There are, however, a few differences between these models, since they are developed for specific purposes. Thus, the main objective of this study is to develop a cybersecurity awareness model specifically designed for Saudi students to protect them from social engineering attacks. The design science methodology was utilized in this study. The proposed model consists of four main stages: education and training, developing policies and guidelines, improving Saudi schools’ security, as well as monitoring and evaluation. The model introduced can ensure the safety and privacy of students, teachers, and staff across different social platforms.

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Section: Methodsmentioning

confidence: 99%

A Cybersecurity Awareness Model for the Protection of Saudi Students from Social Media Attacks

Alotibi

2024

Eng. Technol. Appl. Sci. Res.

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“…Authors in [31] presented IoT engineering applications in the humanitarian context, entailing disaster management, healthcare monitoring, environmental monitoring, and infrastructure development. Multi-level blockchain security architectures were proposed in [32,33] to simplify implementation while bolstering network security. A more recent study [34], examined the challenges and strategies related to cloud computing and IoT security.…”

Section: Inclusionmentioning

confidence: 99%

IoT Security Model for Smart Cities based on a Metamodeling Approach

Alotaibe

2024

Eng. Technol. Appl. Sci. Res.

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Security solutions for the Internet of Things (IoT) in smart cities are complex and require a comprehensive approach to success. Several models and frameworks have been developed focusing on IoT security. Some deal with access controls and security and some with authentication and authorization in various forms. Literature still lacks a comprehensive IoT security model for smart cities, which can support the implementation of IoT. Accordingly, this study has set two objectives: to explore the present studies in IoT security for smart cities and to develop an IoT security model for smart cities based on the metamodeling approach. According to the findings of the study, the existing IoT security models for smart cities consider seven security aspects: authentication and authorization, device management, intrusion detection and prevention, device integrity, secure communication, secure data storage, and response to security incidents. The model developed in this study, called IoT Security Metamodel (IoTSM), combines these aspects. IoTSM captures the main qualities of IoT security practices in smart cities through domain security processes.

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“…A classification of uncertain event logs and models is defined and the challenges posed by uncertainty for process discovery and consistency checking are examined. Chaotic activity detection facilitates the improvement of business process quality and promotes early prediction of various fraudulent activities [12], such as credit card theft [13], false insurance claims [14], and healthcare systems [15]. Detecting chaotic activity in data streams is extremely challenging because event logs are by nature highly variable [2].…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Behavioral Closeness Filtering Chaotic Activity Method

Li,

Fang,

Zuo

2024

Mathematics

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In the era of big data, one of the key challenges is to discover process models and gain insights into business processes by analyzing event data recorded in information systems. However, Chaotic activity or infrequent behaviors often appear in actual event logs. Process models containing such behaviors are complex, difficult to understand, and hide the relevant key behaviors of the underlying processes. Established studies have generally achieved chaotic activity filtering by filtering infrequent activities or activities with high entropy values and ignoring the behavioral relationships that exist between activities, resulting in effective low-frequency behaviors being filtered. To solve this problem, this paper proposes an entropy-based behavioral closeness filtering of chaotic activities method. Firstly, based on the behavior profile theory of high-frequency logging activities, the process model is constructed by combining the feature network and the module network. Then, the identification of suspected chaotic activity sets is achieved through the Laplace entropy value. Next, a query model is built based on logs containing suspicious chaotic activity. Finally, based on the succession relationship, the behavioral closeness of the query model and the business process model is analyzed to achieve the goal of accurately filtering chaotic activities to retain behaviors beneficial to the process. To evaluate the performance of the method, we validated the effectiveness of the proposed algorithm in synthetic logs and real logs, respectively. Experimental results showed that the proposed method performs better in precision after filtering chaotic activities.

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A Survey and a Credit Card Fraud Detection and Prevention Model using the Decision Tree Algorithm

Cited by 11 publications

References 34 publications

A Cybersecurity Awareness Model for the Protection of Saudi Students from Social Media Attacks

A Cybersecurity Awareness Model for the Protection of Saudi Students from Social Media Attacks

IoT Security Model for Smart Cities based on a Metamodeling Approach

Entropy-Based Behavioral Closeness Filtering Chaotic Activity Method

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