AI-Driven Cloud Security: Examining the Impact of User Behavior Analysis on Threat Detection

Asian Journal of Research in Computer Science 17(3):57-74 (2024)

18 Pages Posted: 16 Feb 2024

See all articles by Samuel Oladiipo Olabanji

Samuel Oladiipo Olabanji

Midcontinent Independent System Operator (MISO energy)

Yewande Marquis

University of the Cumberlands

Chinasa Susan Adigwe

University of the Cumberlands

Samson Abidemi Ajayi

University of Ilorin

Tunboson Oyewale Oladoyinbo

University of Maryland University College (UMUC)

Oluwaseun Oladeji Olaniyi

University of the Cumberlands

Date Written: January 29, 2024

Abstract

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.

Note:

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Keywords: AI-driven user behavior analysis, cloud security, traditional security measures, cyber threat detection, predictive capabilities, hybrid security strategy, cloud computing environments

Suggested Citation

Olabanji, Samuel Oladiipo and Marquis, Yewande and Adigwe, Chinasa Susan and Ajayi, Samson Abidemi and Oladoyinbo, Tunboson Oyewale and Olaniyi, Oluwaseun Oladeji, AI-Driven Cloud Security: Examining the Impact of User Behavior Analysis on Threat Detection (January 29, 2024). Asian Journal of Research in Computer Science 17(3):57-74 (2024), Available at SSRN: https://ssrn.com/abstract=4709384 or http://dx.doi.org/10.2139/ssrn.4709384

Samuel Oladiipo Olabanji

Midcontinent Independent System Operator (MISO energy)

Yewande Marquis

University of the Cumberlands ( email )

Chinasa Susan Adigwe

University of the Cumberlands

Samson Abidemi Ajayi

University of Ilorin ( email )

Tunboson Oyewale Oladoyinbo

University of Maryland University College (UMUC)

Oluwaseun Oladeji Olaniyi (Contact Author)

University of the Cumberlands ( email )

6178 College Station Drive
Williamsburg, KY 40769
United States

HOME PAGE: http://www.ucumberlands.edu

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