Malicious URL Detection Backdoor Testing via Slash Manipulation
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Solution Overview
Problem
Existing malicious URL detection systems, particularly those based on machine learning and neural networks, are vulnerable to backdoor attacks, which are not adequately addressed by conventional methods, leading to security vulnerabilities.
Innovation Solution
A backdoor attack method that exploits the browser's insensitivity to the number of slashes in URLs by manipulating the position of separators in URLs to generate adversarial samples, which are used to train a neural network model, testing the attack strength and updating the attack mode in real-time to achieve a desired success rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a machine learning model or neural network model is used for malicious URL detection, then the ability for detecting unknown malicious URLs is greatly improved, but the model becomes vulnerable to backdoor attacks and poisoning attacks
Solution Approach 1:
The patent applies preliminary anti-action by introducing a backdoor attack method that proactively identifies and exploits vulnerabilities in malicious URL detection models before they can be maliciously exploited. The method pre-trains models with adversarial samples containing backdoor triggers, enabling the system to detect and defend against potential poisoning attacks by understanding their structure and impact mechanisms in advance.
Solution Approach 2:
The patent converts the harmful effect of backdoor attacks into a beneficial security testing mechanism. By deliberately introducing controlled backdoor triggers into training data and evaluating model performance, the method transforms potential security vulnerabilities into a diagnostic tool that reveals model weaknesses and guides the development of more robust detection systems.
2Reliability
If adversarial samples are added to training data to test model vulnerability, then the security evaluation capability is improved, but the training data quality and model performance may deteriorate
Solution Approach 1:
The patent applies segmentation by separating the training dataset into distinct components: clean training data for model learning and adversarial samples with controlled backdoor triggers for security evaluation. This segmentation allows the system to maintain high training data quality while independently assessing model vulnerability, preventing adversarial samples from corrupting the learning process.
Solution Approach 2:
The patent applies local quality by applying backdoor triggers only to specific portions of the training data designated for security evaluation, rather than uniformly across all training samples. This localized application ensures that the majority of training data remains high-quality and uncontaminated, while still enabling comprehensive security testing through the targeted adversarial samples.
Data Source
AI summary
The disclosure provides a backdoor attack method and apparatus for a malicious URL detection system. The backdoor attack method includes: obtaining original URL samples of backdoor URL samples to be generated; determining position information of separator slashes in each URL of the original URL samples, and obtaining a position number result by numbering the position information; determining a backdoor attack mode of the malicious URL detection system based on the position number result, and generating the backdoor URL samples corresponding to the original URL samples by using the backdoor attack mode; and constructing a training set based on a preset ratio of the backdoor URL samples, training a neural network model by using the training set, and testing an attack strength of the malicious URL detection system in the backdoor attack mode by using the trained neural network model to obtain a real-time attack success rate.