首页> 外文期刊>IEEE transactions on mobile computing >Cloud-Based Malware Detection Game for Mobile Devices with Offloading
【24h】

Cloud-Based Malware Detection Game for Mobile Devices with Offloading

机译:适用于具有卸载功能的移动设备的基于云的恶意软件检测游戏

获取原文
获取原文并翻译 | 示例

摘要

As accurate malware detection on mobile devices requires fast process of a large number of application traces, cloud-based malware detection can utilize the data sharing and powerful computational resources of security servers to improve the detection performance. In this paper, we investigate the cloud-based malware detection game, in which mobile devices offload their application traces to security servers via base stations or access points in dynamic networks. We derive the Nash equilibrium (NE) of the static malware detection game and present the existence condition of the NE, showing how mobile devices share their application traces at the security server to improve the detection accuracy, and compete for the limited radio bandwidth, the computational and communication resources of the server. We design a malware detection scheme with Q-learning for a mobile device to derive the optimal offloading rate without knowing the trace generation and the radio bandwidth model of other mobile devices. The detection performance is further improved with the Dyna architecture, in which a mobile device learns from the hypothetical experience to increase its convergence rate. We also design a post-decision state learning-based scheme that utilizes the known radio channel model to accelerate the reinforcement learning process in the malware detection. Simulation results show that the proposed schemes improve the detection accuracy, reduce the detection delay, and increase the utility of a mobile device in the dynamic malware detection game, compared with the benchmark strategy.
机译:由于在移动设备上进行准确的恶意软件检测需要快速处理大量应用程序跟踪,因此基于云的恶意软件检测可以利用安全服务器的数据共享和强大的计算资源来提高检测性能。在本文中,我们研究了基于云的恶意软件检测游戏,其中移动设备通过基站或动态网络中的访问点将其应用程序跟踪卸载到安全服务器。我们推导了静态恶意软件检测游戏的Nash平衡(NE),并给出了该NE的存在条件,显示了移动设备如何在安全服务器上共享其应用程序跟踪以提高检测精度,并竞争有限的无线电带宽,服务器的计算和通信资源。我们为移动设备设计了一种具有Q学习的恶意软件检测方案,以在不了解其他移动设备的跟踪生成和无线带宽模型的情况下得出最佳卸载率。使用Dyna架构可进一步提高检测性能,其中移动设备将从假设的经验中学习以提高其收敛速度。我们还设计了一种基于决策后状态学习的方案,该方案利用已知的无线电信道模型来加速恶意软件检测中的强化学习过程。仿真结果表明,与基准策略相比,该方案提高了检测精度,减少了检测延迟,提高了移动设备在动态恶意软件检测游戏中的实用性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号