A machine learning based intrusion detection scheme for data fusion in mobile clouds involving heterogeneous client networks

The combination of traditional cloud computing and mobile computing leads to the novel paradigm of mobile cloud computing. Due to the mobility of network nodes in mobile cloud computing, security has been a challenging problem of paramount importance. When a mobile cloud involves heterogeneous client networks, such as Wireless Sensor Networks and Vehicular Networks, the security problem becomes more challenging because the client networks often have different security requirements in terms of computational complexity, power consumption, and security levels. To securely collect and fuse the data from heterogeneous client networks in complex systems of this kind, novel security schemes need to be devised. Intrusion detection is one of the key security functions in mobile clouds involving heterogeneous client networks. A variety of different rule-based intrusion detection methods could be employed in this type of systems. However, the existing intrusion detection schemes lead to high computation complexity or require frequent rule updates, which seriously harms their effectiveness. In this paper, we propose a machine learning based intrusion detection scheme for mobile clouds involving heterogeneous client networks. The proposed scheme does not require rule updates and its complexity can be customized to suit the requirements of the client networks. Technically, the proposed scheme includes two steps: multi-layer traffic screening and decision-based Virtual Machine (VM) selection. Our experimental results indicate that the proposed scheme is highly effective in terms of intrusion detection.