基于深度强化学习的资源受限条件下的DIDS任务调度优化方法
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西安建筑科技大学,西安工程大学,布鲁内尔大学

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TP393.08 C931.2

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国家自然科学基金项目(面上项目,重点项目,重大项目)


An optimization method for DIDS task scheduling under resource-constrained conditions based on deep reinforcement learning
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1.School of Management, Xi'2.'3.an University of Architecture &4.Technology, Xi'5.an 710055, China 2. School of Computer Science, Xi’an Polytechnic University, Xi’an 710048, China 3. Department of Electronic and Electrical Engineering, Brunel University London, Uxbridge UB8 3PH, U.K

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    摘要:

    在节点性能有限的边缘计算环境下进行分布式入侵检测系统(DIDS)的任务分配,是一种典型的资源受限任务调度问题。针对该问题,提出了基于深度强化学习的DIDS低负载任务调度方案。在建立检测引擎性能和数据包负载的评估模型后,首先将任务调度过程描述为马尔科夫决策过程并建立模型的相关空间和价值函数,找到保持DIDS低负载状态的最优策略;然后针对状态和动作空间过大且高维连续的问题,提出通过深度循环神经网络进行函数拟合;最后,为了避免过度的低负载可能造成丢包率上升的问题,提出低负载与丢包率这两个矛盾指标的平衡方法并建立问题模型。实验结果表明所提出的方案可使DIDS在网络变化中动态调节调度策略,保持系统整体的低负载,而安全指标没有明显降低。

    Abstract:

    The task assignment of distributed intrusion detection system (DIDS) in the edge computing environment with limited node performance is a typical resource-constrained task scheduling problem. To solve this problem, a DIDS low-load task scheduling scheme based on deep reinforcement learning is proposed. After establishing the evaluation model of detection engine performance and packet load, the task scheduling process is first described as a Markov decision process and the relevant space and value function of the model are established to find the optimal strategy for maintaining the low-load state of DIDS. To solve the problem of excessively large action space and high-dimensional continuity, a deep recurrent neural network is proposed to perform function fitting. Finally, in order to avoid the problem that excessive low load may cause the packet loss rate to increase, a balancing method of the two contradictory indicators of low load and packet loss rate is proposed and a problem model is established. The experimental results show that the proposed scheme enables DIDS to dynamically adjust the scheduling strategy during network changes, keeping the overall system load low, and the safety indicators are not significantly reduced.

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  • 收稿日期:2021-03-18
  • 最后修改日期:2021-07-23
  • 录用日期:2021-07-30
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