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

机译：一种基于机器学习的基于机器入侵检测方案，用于涉及异构客户端网络的移动云中的数据融合

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摘要

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.
机译：传统云计算和移动计算的组合导致了移动云计算的新颖范式。由于网络节点在移动云计算中的移动性，安全性是至关重要的具有挑战性的问题。当移动云涉及异构客户端网络（例如无线传感器网络和车辆网络）时，安全问题变得更具挑战性，因为客户端网络在计算复杂度，功耗和安全级别方面通常具有不同的安全要求。为了安全地收集和融合来自这种复杂系统的异构客户端网络中的数据，需要设计新的安全方案。入侵检测是涉及异构客户端网络的移动云中的关键安全功能之一。可以在这种类型的系统中使用各种不同的基于规则的入侵检测方法。然而，现有的入侵检测方案导致高计算复杂性或需要频繁的规则更新，这严重损害了它们的有效性。本文提出了一种基于机器学习的基于机器学习，用于涉及异构客户端网络的移动云的入侵检测方案。该方案不需要规则更新，并且可以自定义其复杂性以满足客户端网络的要求。从技术上讲，所提出的方案包括两个步骤：多层流量筛选和基于决策的虚拟机（VM）选择。我们的实验结果表明，该方案在入侵检测方面非常有效。

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