Anomaly Detection - Recent Advances, AI and ML Perspectives and Applications
Krishna Parimala, Venkata · Artificial intelligence · 2023
Cybersecurity is another critical application area.Javaheri et al. [5] focus on Distributed Denial of Service (DDoS) attacks, providing a comprehensive survey that proposes effective defensive strategies.They emphasize the use of fuzzy logic-based methods as a promising avenue for future research.Zehra et al. [6] discuss the security challenges in Network Function Virtualization (NFV), advocating for machine learning-based anomaly detection techniques to enhance network security.In other specialized applications, Jin et al.[7] provide a comprehensive review of Graph Neural Networks (GNNs) for time series analysis, which includes forecasting, classification, and anomaly detection.Their work serves as a guide to understand the strengths and limitations of using GNNs for time-series data.Patriarca et al. [8] delve into the importance of weather forecasting for aerodrome operations and propose a machine learning-based approach for anomaly detection in historical weather data.Finally, Şengönül et al. [9] explore the use of AI in surveillance video anomaly detection, noting the increasing need for automated systems due to the sheer volume of video data being generated.In summary, while AI and machine learning offer promising solutions for anomaly detection across domains, the effectiveness of these techniques varies significantly.The limitations often arise from domain-specific challenges such as data sparsity, complexity of the anomalies, and computational constraints.Therefore, tailored approaches and continuous research are essential for advancing the field.