计算机科学 ›› 2021, Vol. 48 ›› Issue (5): 60-67.doi: 10.11896/jsjkx.200300127
陈晋音, 邹健飞, 袁俊坤, 叶林辉
CHEN Jin-yin, ZOU Jian-fei, YUAN Jun-kun, YE Lin-hui
摘要: 深度学习方法已被广泛应用于恶意软件检测中并取得了较好的预测精度,但同时深度神经网络容易受到对输入数据添加细微扰动的对抗攻击,导致模型输出错误的预测结果,从而使得恶意软件检测失效。针对基于深度学习的恶意软件检测方法的安全性,提出了一种面向恶意软件检测模型的黑盒对抗攻击方法。首先在恶意软件检测模型内部结构参数完全未知的前提下,通过生成对抗网络模型来生成恶意软件样本;然后使生成的对抗样本被识别成预先设定的目标类型以实现目标攻击,从而躲避恶意软件检测;最后,在Kaggle竞赛的恶意软件数据集上展开实验,验证了所提黑盒攻击方法的有效性。进一步得到,生成的对抗样本也可对其他恶意软件检测方法攻击成功,这验证了其具有较强的攻击迁移性。
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