Anomaly detection for mobile devices in industrial internet

The concept of "Industrial Internet" was first proposed by General Electric in 2012. It aims to promote the intellectualization of the whole service system. However, with the development of the Industrial Internet, some criminals launch attacks on industrial control terminals (such as computers and mobile devices), causing the failure of industrial control terminals or wrong instructions, which resulting in factory losses. Therefore, there is an immediate need to extract valuable information from mobile network streaming, accurately detect abnormal behaviors and timely raise the alarm. In this paper, we propose a method of anomaly detection for mobile devices in Industrial Internet based on knowledge graph and demonstrate the results by using visualization technology. First, we use the optimized data mining algorithm based on frequent item sets to analyse the data, so that our method can accurately detect different kinds of concurrent attacks. Second, this method is able to locate the IP addresses of the attacker and the victim accurately. Third, we design an anomaly alarm module, which can visualize the results in multiple dimensions and assist security administrators to understand complex network situation in real time and take corresponding measures according to the network anomaly.