GPU-Accelerated Outlier Detection for Continuous Data Streams

Abstract

Outlier detection or anomaly detection is applied in numerous applications, such as fraud detection, network intrusion detection, manufacturing, and environmental monitoring. Due to the continuous and dynamic characteristics of streaming data, outlier detection over data streams becomes a very challenging task. When analyzing real-time data streams, it is typically impossible to store the entire set of data due to space limitations. Also due to the high data rates, it is necessary to produce the results in a limited amount of time. Parallel processing power of Graphics Processing Units (GPUs) can be used to accelerate the outlier detection process, and thus address the challenges of outlier detection over data streams. This paper proposes a GPU-accelerated outlier detection algorithm for continuous data streams using kernel density estimation approach. Experiments show that the proposed SOD_GPU algorithm is efficient for detecting outliers in high-dimensional, high-speed data streams, and produces results in a timely manner without compromising the outlier detection accuracy. The proposed method achieved up to 20X speedup compared to a respective multi-core CPU implementation, and the speedup increases with the number of data attributes and the input data rate.