Dark Web Identification via Traffic Direction Sequence Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for identifying dark web websites struggle when users access multiple tabs simultaneously, as they fail to distinguish dynamic traffic patterns and are vulnerable to defensive measures, leading to reduced identification accuracy.
Innovation Solution
A method that extracts direction sequence features from network traffic packets, divides them into subsequence features using sliding windows, and employs a neural network model with a multi-head top-m attention layer to analyze correlations and determine the probability of accessing a target website, incorporating convolutional layers and Batch Norm layers for robust feature extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional website identification methods are used, then identification can be performed on single-page traffic, but identification accuracy deteriorates when users access multiple tabs simultaneously
Solution Approach 1:
The patent divides the multi-tab traffic identification problem into multiple single-tab sub-problems by detecting tab boundaries and processing each tab's traffic separately. This segmentation allows the system to maintain high identification accuracy for each individual tab while handling multiple concurrent tabs, resolving the contradiction between single-page accuracy and multi-tab adaptability.
2Reliability
If defense methods such as BuFLO, Tamaraw, or CS-BuFLO are applied to protect traffic privacy, then traffic pattern protection is improved, but page loading delay increases significantly
Solution Approach 1:
The patent extracts and removes padding packets from the traffic stream before identification, separating the protective padding from the actual content traffic. This allows the system to maintain traffic pattern protection while eliminating the time delay caused by processing unnecessary padding packets, thus resolving the contradiction between protection reliability and time loss.
3Reliability
If padding packets are added to protect traffic patterns (as in Tamaraw and CS-BuFLO), then traffic privacy protection is improved, but significant delays occur in loading web pages
Solution Approach 1:
The patent converts the harmful effect of padding packets (which cause delays) into a beneficial feature by using them as markers to detect tab boundaries. The previously problematic padding packets now serve as useful signals for identifying when one tab ends and another begins, resolving the contradiction between privacy protection and loading speed.
4Device complexity
If lightweight defense methods like WTF-PAD or Front are used, then overhead is reduced, but they cannot defend against advanced dark website identification methods
Solution Approach 1:
The patent implements a dynamic identification system that adapts to different defense strategies by detecting traffic characteristics and adjusting identification methods accordingly. This dynamic approach maintains low overhead while effectively countering various defense methods, resolving the contradiction between defense complexity and effectiveness.
Data Source
AI summary
The present disclosure provides a method and an apparatus for identifying a dark web website in a scenario where a plurality of pages are accessed simultaneously. The method includes: obtaining browsed network traffic packets of websites to be identified, and extracting direction sequence features from the network traffic packets; dividing the direction sequence features into a plurality of subsequence features based on a plurality of sliding windows, and inputting the plurality of subsequence features into a neural network model to extract preset pattern features; analyzing a correlation of the preset pattern features using a target website identification model to obtain a probability result of a target website being accessed; obtaining a target website identification result in the website to be identified based on the probability results and a preset classification model.