Anomaly detection‐based intelligent computing in internet of things and network applications

Abstract

With the popularization of smart and Internet of Things (IoT) devices, a tremendous amount of data is being produced by devices distributed everywhere on the globe. Meanwhile, many new smart applications are appearing which require Internet connection to operate. Artificial intelligence (AI)-driven IoT and network applications has been widely recognized as a promising solution for smart scenarios. 1,2 For IoT and emerging smart applications (eg, smart city, smart healthcare, and smart agriculture), it is often unfeasible to collect thousands of uniformly distributed data from heterogeneous distributed devices to train a reasonable model. Such systems typically only use relatively a small amount of samples to train a goal-oriented model which could be heavily affected by minor anomalies. Therefore, in the IoT and emerging network applications, it is urgent to detect anomalies from the collected samples to alleviate the side effect on model training. 3,4 Anomalies can be defined as the patterns that do not conform to expected behavior. 5,6 Actually, anomalies can appear in different forms in real applications, f.i., illegal intrusions on the Internet of services, 7 irregular behaviors in the scenario of smart city, 8 and abnormal events in smart agriculture. 9 Anomaly detection has been researched for several decades and many anomaly detection methods have been proposed so far. However, an important open question to be answered is how to use anomaly detection methods to find minor meaningful patterns in real applications. This special issue will focus on identifying minor anomalies in real applications by using state-of-the-art anomaly detection methods. Examples of applications can be found in many fields, f.i.,

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