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AI-based anomaly detection methods leverage the power of algorithms and statistical models to learn and recognize patterns in data, enabling them to automatically detect anomalies that might be difficult to identify through traditional approaches.
But AI-based anomaly detection methods are only as effect as the data they are trained on. That cause a critical limitation when applying AI-based anomaly detection methods in the real-world network environments.
Moreover, the landscape of malicious attacks is constantly evolving, giving rise to new forms and types of threats. To build effective anomaly detection systems, it is essential to have access to data that accurately reflects the latest network conditions and includes the most recent malicious attacks.
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IEEE/ICACT20240309 Slide.03
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