Abstract The development of Industrial Internet of Things (IIoT) systems has been widely used in the fields of security monitoring, intelligent transportation, environmental monitoring, etc. However, the sensors are damaged due to continuous use and normal wear and tear, resulting in hidden anomalies in the collected and recorded sensing data. The detection of anomalies in IIoT data is a challenging problem that is currently of great importance to both academia and industry. An anomaly detection algorithm (Locality Sensitive Bloom Filter (LSBF)) based on a locality sensitive Bloom Filter model is proposed. The proposed algorithm is based on a locality sensitive Bloom Filter model (LSBF), which uses the fast Johnson-Lindenstrauss transform based on spatial partitioning (SP-FJLT) to hash map the data, and then a Mutual Competition (MC) strategy to de-dry the data. The experimental results on dataset SIFT, MNIST and FMA show that the proposed algorithm exhibits high detection accuracy and low false alarm rate compared with the current mainstream anomaly detection algorithms, and can be effectively used for anomaly detection of IIoT data.
Received: 28 June 2021
Published: 17 September 2021