Abstract
Anomalous behavior detection in many applications is becoming more and more important, especially for computer security and sensor networks domains, in which data are typical time-series. However, the sequence anomaly detection for time-series data exists lots of problems, for example, there is no anomalous point in time series sequence but the whole sequence may be anomalous. In this paper, we use the sliding window framework to split time-series into sequences and taking into account the time-series statistical features of the sequence, proposing a novel sequence anomaly detection algorithm based on iForest, namely iForestFS. The experimental results are performed on three real-world data sets derived from UCI repository demonstrate that the proposed algorithm can effectively detect anomalous sequence of time-series data.
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Weng, Y., Liu, L. (2019). A Sequence Anomaly Detection Approach Based on Isolation Forest Algorithm for Time-Series. In: Hu, C., Yang, W., Jiang, C., Dai, D. (eds) High-Performance Computing Applications in Numerical Simulation and Edge Computing. HPCMS HiDEC 2018 2018. Communications in Computer and Information Science, vol 913. Springer, Singapore. https://doi.org/10.1007/978-981-32-9987-0_17
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DOI: https://doi.org/10.1007/978-981-32-9987-0_17
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