Research On Association Rule Mining Algorithm Based On Incrementand Implement In Smart Phone Viruses Detection

Nowadays, smart phone viruses become more destructive with increasing spreading speed, which put a strain on the limited wireless network resources and also a threat to information privacy. Consequently, an antivirus mechanism tailor-made for mobile communication network is of significant importance today. This paper firstly does an exploratory research in applying the Increment Association Rule Mining Algorithm to smart phone virus detection mechanism, and further presents an effective method to implement it. This work is supported by the enterprise-commissioned project Smart Phone Viruses detection System, and the main task is to study andimplement the Association Rule Mining (ARM) module in order to provide a cellphone virus detection solution.The main work of this paper is stated as follows. Firstly, the paper presents the definition and key features of smart phone viruses, and the classification of the viruses according to the damage caused. Also it surveys the mainstream anti-phone-virus technique used by the network side. Secondly, the paper presents the basic theory of ARM algorithm and its general steps, sort existed algorithms into two categories according to with or without candidate items, and compare their execution features, advantages and disadvantages; also analyze the multi-value attributes originated from the supporting project, as well as the existed mining algorithms for this specific attribute. It is found that the multi-value-attribute-aimed ARM algorithms own several basic differences with common ARM algorithm, which shows the importance of a new measure of association rules. For this purpose, some measures commonly used are analyzed, and the features of each are presented. Thirdly, improved algorithms for Apriori algorithm and FUP algorithm are put forward, based on new database operation technique, increment update technique and the features of data collected from the supporting project. The test shows that the improved algorithms bring significant enhancement in execution efficiency. At last, the framework of design and realization of the ARM module in smart phone virus detection system is presented, and all the test results are elaborated.The contribution of this work is that it puts forward two algorithms which bring great enhancement in association rule mining performance and increment update performance:an improved prearranged-attributes-support-statistic-based ARM algorithm, and an improved candidate-frequent-items-based ARM algorithm. These two improved algorithms can be used in ARM module in cellphone virus detection system, showing a wide application foreground in the active defense mechanism for mobile communication network.