基于自动扰动策略的静态恶意样本生成模型

STATIC MALICIOUS SAMPLE GENERATION MODEL BASED ON AUTOMATIC PERTURBATION STRATEGY

  • 摘要: 现有的扰动方法在增加静态恶意软件逃避率的同时导致其在目标主机无法运行。针对这种情况,该文提出一种基于自动扰动策略的静态恶意样本生成模型。该策略搭建行动空间,构建自动扰动策略动作库,采用启发式组合方式,将基础策略与历史最优扰动动作进行不同组合,输出最优策略到智能体,最终得到面向静态恶意软件对抗样本生成模型。其次,定义逃避率及可用率的调和平均数Harmonic Means of Evasion and Availability, HMEA指标,以量化静态恶意软件对抗样本的有效性。在VirusShare数据集上的实验表明,该方法逃避率达42.00%, 可用率提升至99.50%, HMEA达59.07%, 证明了该方法的有效性。

     

    Abstract: Existing perturbation methods increase the evasion rate of static malware while making it impossible to run on the target host. In view of this situation, a static malicious sample generation model based on automatic perturbation strategy is proposed. The strategy constructed the action space, constructed the automatic perturbation strategy action library, adopted the heuristic combination method, combined the basic strategy and the historical optimal perturbation action in different ways, outputted the optimal strategy to the agent, and obtained the static malware adversarial sample generation model. We defined the harmonic mean of evasion and availability HMEA metrics to quantify the effectiveness of static malware adversarial examples. Experiments on the VirusShare dataset show that the evasion rate of the method reaches 42. 00%, the availability rate increases to 99. 50%, and the HMEA reaches 59. 07%, proving the effectiveness of the method.

     

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