Abstract
Anomaly detection algorithms (ADA) have been widely used as services in many maintenance monitoring platforms. However, there are numerous algorithms that could be applied to these fast changing stream data. Furthermore, in IoT stream data due to its dynamic nature, the phenomena of conception drift happened. Therefore, it is a challenging task to choose a suitable anomaly detection service (ADS) in real time. For accurate online anomalous data detection, this paper developed a service selection method to select and configure ADS at run-time. Initially, a time-series feature extractor (Tsfresh) and a genetic algorithm-based feature selection method are applied to swiftly extract dominant features which act as representation for the stream data patterns. Additionally, stream data and various efficient algorithms are collected as our historical data. A fast classification model based on XGBoost is trained to record stream data features to detect appropriate ADS dynamically at run-time. These methods help to choose suitable service and their respective configuration based on the patterns of stream data. The features used to describe and reflect time-series data’s intrinsic characteristics are the main success factor in our framework. Consequently, experiments are conducted to evaluate the effectiveness of features closed by genetic algorithm. Experimentations on both artificial and real datasets demonstrate that the accuracy of our proposed method outperforms various advanced approaches and can choose appropriate service in different scenarios efficiently.
Highlights
With the growth of the Internet of ings (IoT), the sensor or stream data is bound to be collected at tremendous speed
Researchers like Bu et al [14] state that for some kinds of stream data simple Anomaly detection algorithms (ADA) may perform well compared to some multifaceted algorithms such as deep learning. e pattern of stream data can be recognised in time which overlays the way for future algorithm selection in modern microservice architecture recognised as a service selection. e main contribution of our work focuses on the extraction of features to characterise stream data and based on these features select suitable algorithm service
It is unfeasible to build a universal method to detect all types of anomalies in IoT stream data; we attempt to discriminate the data pattern and adjust appropriate anomaly detection service (ADS)
Summary
With the growth of the Internet of ings (IoT), the sensor or stream data is bound to be collected at tremendous speed. Erefore, for faster and more accurate anomaly detection, it is obligatory to choose an appropriate service for different stream data dynamically at run-time. For effective handling of anomalies from various stream data, based on the above observation, in this paper, an Anomaly Detection via Service Selection (ADSS) framework was proposed. A fast classifier based on the XGBoost algorithm is trained to record features of stream data in order to detect appropriate ADS dynamically at run-time. Due to the presence of the best classifier, our ADSS method can identify the dynamics of data stream patterns of newly appearing stream of data and choose and configure the suitable service. (i) In this paper, we develop a method that facilitates IoT-based systems to automatically choose appropriate services using the existing data features in order to detect an anomaly.
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