AI-Driven Cloud Security: Examining the Impact of User Behavior Analysis on Threat Detection
Samuel Oladiipo Olabanji, Yewande Alice Marquis, Chinasa Susan Adigwe, Samson Abidemi Ajayi, Tunbosun Oyewale Oladoyinbo, Oluwaseun Oladeji Olaniyi
Asian Journal of Research in Computer Science · pp. 57–74 · Published 29 Jan 2024
10.9734/ajrcos/2024/v17i3424Abstract
This study explores the comparative effectiveness of AI-driven user behavior analysis and traditional security measures in cloud computing environments. It specifically examines their accuracy, speed, and predictive capabilities in detecting and responding to cyber threats. As reliance on cloud-based solutions intensifies, the integration of Artificial Intelligence (AI) and machine learning into cloud security has become increasingly vital. The research focuses on how AI-driven security systems, with their advanced pattern recognition and anomaly detection, compare to traditional methods in identifying deviations from standard user behaviors in cloud settings. Employing a quantitative approach, the study utilizes a detailed survey strategy, targeting cybersecurity professionals across multiple industries, including finance, healthcare, information technology, retail, and government sectors. The survey, comprising both closed-ended and Likert-scale questions, is designed to elicit nuanced responses on the perceptions and experiences of these professionals regarding AI-driven versus traditional security methods in cloud environments. The data, collected from a purposive sample of 243 cybersecurity personnel, is analyzed using multiple regression analysis. This analysis facilitates an understanding of the impact of different security systems on the efficacy of threat detection and response in cloud contexts. The results indicate that while both AI-driven and traditional methods significantly improve threat detection accuracy, traditional methods show a slight edge. Conversely, AI-driven systems demonstrate notably superior predictive capabilities and overall enhanced security performance. These findings suggest the necessity of a hybrid security strategy in cloud computing. Such an approach would combine the advanced capabilities of AI, particularly in predictive analytics and adaptability, with the rapid and reliable responses of traditional methods. This integrated strategy is proposed to effectively address the unique challenges posed by the dynamic and complex nature of cloud-based cyber threats. This study provides valuable insights for both businesses and IT professionals on the effective integration of AI-driven security measures in cloud environments. It highlights the evolving role of AI in cloud security and the importance of maintaining a balance between innovative AI approaches and established traditional methods to create a robust, comprehensive cloud security framework.
Cited by 94
Chinasa Susan Adigwe, Oluwaseun Oladeji Olaniyi, Oladotun Opeoluwa Olagbaju, Folashade Gloria Olaniyi · Asian Journal of Economics, Business and Accounting · 2024
Samuel Oladiipo Olabanji, Oluseun Babatunde Oladoyinbo, Christopher Uzoma Asonze, Tunbosun Oyewale Oladoyinbo, Samson Abidemi Ajayi, Oluwaseun Oladeji Olaniyi · Asian Journal of Economics, Business and Accounting · 2024
Chinasa Susan Adigwe, Oluwaseun Oladeji Olaniyi, Samuel Oladiipo Olabanji, Olalekan Jamiu Okunleye, Nanyeneke Ravana Mayeke, Samson Abidemi Ajayi · Asian Journal of Economics, Business and Accounting · 2024
Samuel Oladiipo Olabanji, Tunbosun Oyewale Oladoyinbo, Christopher Uzoma Asonze, Chinasa Susan Adigwe, Olalekan Jamiu Okunleye, Oluwaseun Oladeji Olaniyi · Asian Journal of Economics, Business and Accounting · 2024
Chinasa Susan Adigwe, Nanyeneke Ravana Mayeke, Samuel Oladiipo Olabanji, Olalekan Jamiu Okunleye, Princess Chimmy Joeaneke, Oluwaseun Oladeji Olaniyi · Asian Journal of Economics, Business and Accounting · 2024
Onyinye Obioha-Val · Asian Journal of Research in Computer Science · 2024
Amaka Debie Samuel-Okon · Asian Journal of Advanced Research and Reports · 2024
Isiaka Akinkunmi Adeyemo, Tunbosun Oladoyinbo, Oluseun Babatunde Oladoyinbo · 2024
Qingqing Ren, Haosheng Li, Haomiao Tian · Lecture Notes in Electrical Engineering · 2025
Nawaf Abdualaziz Almolhis, Mohammed Abdulaziz Alsubhi · Advances in Intelligent Systems and Computing · 2025
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