Combating the Challenges of False Positives in AI-Driven Anomaly Detection Systems and Enhancing Data Security in the Cloud
Omobolaji Olufunmilayo Olateju
*
University of Ibadan, Oduduwa Road, Ibadan, Oyo State, Nigeria.
Samuel Ufom Okon
First Bank DR Congo, Gombe, Democratic Republic of the Congo.
Udochukwu ThankGod Ikechukwu Igwenagu
Prairie View A&M University, 100 University Dr, Prairie View, TX 77446, USA.
Abidemi Ayodotun Salami
Ilinois State University, 100 N University St, Normal, IL 61761, USA.
Tunbosun Oyewale Oladoyinbo
University of Maryland Global Campus, 3501 University Blvd E, Adelphi, MD 20783, USA.
Oluwaseun Oladeji Olaniyi
University of the Cumberlands, 104 Maple Drive, Williamsburg, KY 40769, United States of America.
*Author to whom correspondence should be addressed.
Abstract
Anomaly detection is critical for network security, fraud detection, and system health monitoring applications. Traditional methods like statistical approaches and distance-based techniques often struggle with high-dimensional and complex data, leading to high false positive rates. This study addresses the challenge by investigating advanced AI-driven techniques to reduce false positives and enhance data security within cloud computing environments. This study employs deep learning models, integrates contextual data, and incorporates comprehensive security measures to enhance anomaly detection performance. Data from synthetic sources, such as the NSL-KDD dataset and real-world cloud environments, were utilized to capture user behavior logs, system states, and network traffic. Over 50 academic journals were reviewed, and 21 were selected based on inclusion criteria, such as relevance to AI-driven anomaly detection, empirical performance metrics, and the focus on cloud environments, and exclusion criteria that filtered out studies lacking empirical data or not specific to cloud-based systems. Methodologically, the research involves a comparative analysis of different AI techniques and their impact on false positive rates, accuracy, precision, and recall. The findings demonstrate that deep learning techniques significantly outperform traditional methods, achieving a lower false positive rate and higher accuracy. The results underscore the importance of contextual data and robust security protocols in reliable anomaly detection. This research fills a gap by thoroughly evaluating advanced AI techniques for reducing false positives in cloud environments. The study's significance lies in guiding the development of more effective anomaly detection systems, thereby enhancing security and reliability across various applications. Additionally, organizations should invest in continuously developing and integrating AI-driven anomaly detection systems with comprehensive security measures to improve their effectiveness the study suggests that further study be conducted with large datasets to evaluate the effectiveness of Hybrid anomaly detection systems in detecting and addressing false positives.
Keywords: Anomaly detection, deep learning, cloud security, data security, adaptive techniques