| Summary: | Intelligent anomaly detection is a promising area to discover anomalies as manual processing by human are generally labor-intensive and time-consuming. An effective approach to deal with is essentially to build a classifier system that can reflect the condition of the infrastructure when it tends to behave abnormally, and therefore the appropriate course of action can be taken immediately. In order to achieve aforementioned objective, we proposed to build a dual-staged cascade one class SVM (OCSVM) for water level monitor systems. In the first stage of the cascade model, our OCSVM learns directly on single observation at a time, 1-g to detect point anomaly. Whereas in the second stage, OCSVM learns from the constructed <i>n</i>-gram feature vectors based on the historical data to discover any collective anomaly where the pattern from the <i>n</i>-gram failed to conform to the expected normal pattern. The experimental result showed that our proposed dual-staged OCSVM is able to detect anomaly and collective anomalies effectively. Our model performance has attained remarkable result of about 99% in terms of F1-score. We also compared the performance of our OCSVM algorithm with other algorithms.
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