article · Asian Journal of Research in Computer Science
This study addresses the challenge of high false positive rates in traditional anomaly detection methods, particularly in complex, high-dimensional data and cloud computing environments. It investigates advanced AI-driven techniques, specifically deep learning models, integrated with contextual data and comprehensive security measures. The research used both synthetic datasets, such as NSL-KDD, and real-world cloud environment data for evaluation. A comparative analysis showed that deep learning techniques significantly outperform traditional methods, achieving lower false positive rates and higher accuracy. The findings highlight the importance of contextual data and robust security protocols for reliable anomaly detection, guiding the development of more effective systems to enhance security and reliability.
Anomaly detection is vital for protecting networks and systems from threats like fraud and cyberattacks. By reducing false alarms, this research helps security teams focus on genuine threats, improving efficiency and the overall reliability of cloud-based services. This leads to more secure and trustworthy digital environments for everyone.
This research could lead to the development of more effective anomaly detection software for cloud service providers, cybersecurity companies, and organisations managing critical IT infrastructure. The improved accuracy and reduced false positives offer a clear benefit for enhancing network security, fraud detection, and system health monitoring. This is applied research, demonstrating improved performance, and suggests further development and integration are needed for real-world deployment, indicating it is not yet a near-market product.
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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.
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DOI: 10.9734/ajrcos/2024/v17i6472
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