DP1SVM: a dynamic planar one-class support vector machine for Internet of Things environment

The Internet of Things realisations, such as smart city applications, generates a vast amount of data, and detecting emerging anomalies in such large unlabelled data is a challenge. One-class support vector machines (1SVMs) have ability to detect anomalies by modelling the complex normal patterns in the data. However, they have limitations in terms of higher time complexity. Dynamically updating the 1SVM model for a streaming data by retraining from scratch is a time consuming task. In this work we present a dynamic planar 1SVM that can not only incrementally learn new data as well as remove historic data decrement-ally from the system, but also dynamically adjust the parameters of the algorithm. Evaluation on simulated and benchmark datasets reveals its ability to effectively re-learn with significantly lower computational overhead. Moreover, we analyse its performance for dynamically adjusting the leaning parameters.