论文标题
对抗攻击的游戏理论脆弱性分析
A Game Theoretical vulnerability analysis of Adversarial Attack
论文作者
论文摘要
最近，深度学习已被广泛用于自动化网络域中的各种安全任务。但是，对手在许多情况下操纵数据并降低了部署的深度学习模型的准确性。一个值得注意的例子是欺骗验证码数据以访问基于验证码的分类器，导致关键系统容易受到网络安全攻击的影响。为了减轻这一点，我们提出了一个游戏理论的计算框架，以通过形成同时的两人游戏来分析基于验证码的分类器的脆弱性，策略和结果。我们应用快速梯度符号方法（FGSM）和对验证码数据的一个像素攻击，以模仿可能的网络攻击的真实情况。随后，从游戏理论的角度来解释这种情况，我们代表了库恩树中Stackelberg游戏中的互动，以通过应用分类器的实际预测值来研究玩家的可能行为和行动。因此，我们解释了深度学习应用中的潜在攻击，同时代表了游戏理论前景中可行的防御策略。
In recent times deep learning has been widely used for automating various security tasks in Cyber Domains. However, adversaries manipulate data in many situations and diminish the deployed deep learning model's accuracy. One notable example is fooling CAPTCHA data to access the CAPTCHA-based Classifier leading to the critical system being vulnerable to cybersecurity attacks. To alleviate this, we propose a computational framework of game theory to analyze the CAPTCHA-based Classifier's vulnerability, strategy, and outcomes by forming a simultaneous two-player game. We apply the Fast Gradient Symbol Method (FGSM) and One Pixel Attack on CAPTCHA Data to imitate real-life scenarios of possible cyber-attack. Subsequently, to interpret this scenario from a Game theoretical perspective, we represent the interaction in the Stackelberg Game in Kuhn tree to study players' possible behaviors and actions by applying our Classifier's actual predicted values. Thus, we interpret potential attacks in deep learning applications while representing viable defense strategies in the game theory prospect.
