Service-oriented mobile malware detection system based on mining strategies

The large number of mobile internet users has highlighted the importance of privacy protection. Traditional malware detection systems that run within mobile devices have numerous disadvantages, such as overconsumption of processing resources, delayed updating, and difficulty in intersection. This study proposed a novel detection system based on cloud computing and packet analysis. The system detects the malicious behavior of the mobile malwares through their packets with the use of data mining methods. This approach completely avoids the defects of traditional methods. The system is service-oriented and can be deployed by mobile operators to send alarms to users who have malwares on their devices. To improve system performance, a new clustering strategy called contraction clustering was created. This strategy uses prior knowledge to reduce dataset size. Moreover, a multi-module detection scheme was introduced to enhance system accuracy. The results of this scheme are produced by integrating the detection results of several algorithms, including Naive Bayes and Decision Tree.