Revolutionizing network management with an AI-driven intrusion detection system

Vijay, G. S.; Sharma, Meenakshi; Khanna, Roma

doi:10.31893/multiscience.2023ss0313

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…While AI-driven security measures in cloud computing environments demonstrate superior effectiveness, accuracy, and speed compared to traditional security methods, they are not without limitations. The reliance on data quality, potential biases, and the need for continuous learning are challenges that need addressing [34]. Traditional methods, with their predictability and established protocols, continue to play a role, especially in situations where AI systems may not be sufficiently trained or applicable [28].…”

Section: Effectiveness Of Ai-driven Vs Traditional Security Measuresmentioning

confidence: 99%

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

Olabanji,

Marquis,

Adigwe

et al. 2024

AJRCoS

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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.

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Section: Effectiveness Of Ai-driven Vs Traditional Security Measuresmentioning

confidence: 99%