论文标题
感知哈希应用于TOR域识别
Perceptual Hashing applied to Tor domains recognition
论文作者
论文摘要
TOR DarkNet拥有不同类型的非法含量，这些非法含量由网络安全机构监控。但是，手动对TOR含量进行分类可能会缓慢且容易出错。为了支持这项任务，我们引入了频率优势的邻域结构（F-DNS），这是一种新的感知哈希方法，用于通过其屏幕截图自动对域进行分类。首先，我们使用遵守各种内容保存操作的图像对F-DN进行了评估。我们将它们与原始图像进行了比较，与其他最先进的方法相比，获得更好的相关系数，尤其是在旋转的情况下。然后，我们使用DarkNet使用Service Image-2K（DUSI-2K）应用F-DNS将TOR域分类，该数据集是一个具有活动性TOR服务域的屏幕截图。最后，我们根据图像分类方法和最先进的哈希方法测量了F-DNS的性能。我们的提案在TOR图像中获得了98.75％的精度，超过了所有其他方法。
The Tor darknet hosts different types of illegal content, which are monitored by cybersecurity agencies. However, manually classifying Tor content can be slow and error-prone. To support this task, we introduce Frequency-Dominant Neighborhood Structure (F-DNS), a new perceptual hashing method for automatically classifying domains by their screenshots. First, we evaluated F-DNS using images subject to various content preserving operations. We compared them with their original images, achieving better correlation coefficients than other state-of-the-art methods, especially in the case of rotation. Then, we applied F-DNS to categorize Tor domains using the Darknet Usage Service Images-2K (DUSI-2K), a dataset with screenshots of active Tor service domains. Finally, we measured the performance of F-DNS against an image classification approach and a state-of-the-art hashing method. Our proposal obtained 98.75% accuracy in Tor images, surpassing all other methods compared.
