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| Title | Enhancing Cybersecurity Through Advanced Fraud And Anomaly Detection Techniques: A Systematic Review |
|---|---|
| ID_Doc | 23767 |
| Authors | Olowe O.T.; Adebiyi A.A.; Marion A.O.; Tobi O.M.; Olaniyan D.; Olaniyan J.; Emmanuel A.; Akindeji K. |
| Year | 2024 |
| Published | International Conference on Science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024 |
| DOI | http://dx.doi.org/10.1109/SEB4SDG60871.2024.10629767 |
| Abstract | Fraud and anomaly detection in cybersecurity have become critical areas of research, prompting a comprehensive review of recent developments and model effectiveness. This study investigates prevalent trends and challenges in fraud and anomaly detection, focusing on data science approaches within e-commerce and smart cities. Through the analysis, key factors influencing detection efficacy, such as deep learning models and hybrid approaches, are examined. Findings highlight the multidimensional nature of cyber threats across various domains and underscore the importance of proactive security measures, including robust authentication, network fortification, and employee training. This paper contributes to enhancing awareness and understanding of evolving cyber threats while offering actionable insights for bolstering detection capabilities and mitigating risks. © 2024 IEEE. |
| Author Keywords | Algorithm; Anomaly; Cybersecurity; Detection; Fraud; Network |
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