论文标题
使用转移学习对工业控制系统屏幕截图进行分类
Classification of Industrial Control Systems screenshots using Transfer Learning
论文作者
论文摘要
工业控制系统在很大程度上取决于安全和监视协议。为此，有几种工具可用于侦察漏洞，并从各种控制面板中获取屏幕截图，以供以后分析。但是，它们不能充分地将图像分类为特定的对照组，这可能会由手动操作员执行的操作困难。为了解决此问题，我们将转移学习与五个CNN体系结构进行了预先培训的CNN架构，以确定哪种最佳分类是从工业控制系统中获得的屏幕截图。使用337个手动标记的图像，我们训练这些架构，并在准确性以及CPU和GPU时间上研究其性能。我们发现，Mobilenetv1是基于其97,95％的F1分数的最佳体系结构，并且每图像为0.47秒，其速度为CPU。在时间至关重要且可用GPU的系统中，VGG16是可取的，因为它需要0.04秒才能处理图像，但性能下降至87,67％。
Industrial Control Systems depend heavily on security and monitoring protocols. Several tools are available for this purpose, which scout vulnerabilities and take screenshots from various control panels for later analysis. However, they do not adequately classify images into specific control groups, which can difficult operations performed by manual operators. In order to solve this problem, we use transfer learning with five CNN architectures, pre-trained on Imagenet, to determine which one best classifies screenshots obtained from Industrial Controls Systems. Using 337 manually labeled images, we train these architectures and study their performance both in accuracy and CPU and GPU time. We find out that MobilenetV1 is the best architecture based on its 97,95% of F1-Score, and its speed on CPU with 0.47 seconds per image. In systems where time is critical and GPU is available, VGG16 is preferable because it takes 0.04 seconds to process images, but dropping performance to 87,67%.
