URL Attack Detection in Encrypted Egress Traffic
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Solution Overview
Problem
Existing firewalls struggle to enforce password constraint policies for external site authentication and detect scanning and attacking uniform resource locators (URLs) due to encrypted tunneling protocols, leading to security vulnerabilities and difficulty in distinguishing between benign and malicious traffic.
Innovation Solution
Implement a firewall with a trusted man-in-the-middle technique to decrypt SSL sessions, apply password constraint checks, and use machine learning models to identify malicious URL requests based on multiple features, including K-nearest neighbors technique.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If SSL sessions are encrypted to protect data transmission, then security is improved, but the ability to detect and enforce password policies on external site authentication is lost
Solution Approach 1:
The patent introduces a man-in-the-middle decryption system that acts as an intermediary between the encrypted SSL traffic and the firewall. This intermediary component decrypts SSL sessions to enable password constraint policy enforcement while maintaining security through controlled access to decrypted data. The system selectively decrypts only authentication-related traffic for policy checking while preserving end-to-end encryption for other communications.
2Reliability
If traditional firewalls are used to filter traffic, then basic network security is maintained, but scanning and attacking URL detection capability is insufficient
Solution Approach 1:
The patent replaces traditional mechanical rule-based firewall filtering with machine learning-based detection systems. The system employs trained machine learning models that analyze multiple features of URL requests (such as request patterns, timing, source information) to automatically distinguish between benign and malicious traffic. This substitution enables more precise detection of scanning and attacking URLs compared to conventional signature-based or rule-based approaches.
3Measurement precision
If machine learning models with multiple features are used to detect malicious URLs, then detection accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent implements feature pre-computation and pre-filtering mechanisms that prepare detection data in advance before it reaches the machine learning models. By pre-processing network traffic to extract and organize relevant features (such as URL patterns, request metadata, source IP information) beforehand, the system reduces the computational burden on the machine learning models during real-time detection. This preliminary action enables accurate multi-feature analysis while maintaining efficient processing speeds.
Data Source
AI summary
Techniques for detecting scanning and attacking uniform resource locators in network traffic are disclosed. A system, process, and/or computer program product for detecting scanning and attacking uniform resource locators in network traffic includes monitoring egress traffic from an enterprise network, determining whether a uniform resource locator (URL) request is associated with scanning and attacking egress traffic based on one or more features, and performing an action in response to a determination that the URL request is associated with the scanning and attacking egress traffic from the enterprise network.