Encrypted HTTPS Traffic Fingerprinting for Hacking Tool Detection
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Solution Overview
Problem
Existing methods for identifying HTTPS traffic are costly and inaccurate, particularly due to the challenges posed by TLS encryption, which obscures malicious attack traffic, and machine learning-based approaches suffer from high false positives.
Innovation Solution
A traffic identification method that utilizes a fingerprint blacklist database and a tool genetic database to extract and match target features from HTTPS traffic, accurately identifying hacking tools by matching against genetic features, eliminating the need for dedicated proxy tools and reducing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If TLS encryption is used to protect traffic, then security capability is enhanced, but malicious attack traffic becomes difficult to distinguish
Solution Approach 1:
The patent introduces TLS fingerprint features as an intermediary characteristic that bridges the gap between encrypted traffic and attack detection. Instead of attempting to decrypt TLS traffic, the system extracts fingerprint features from the encrypted stream itself, which serve as mediators for identifying malicious tools without compromising the encryption's security benefits
Solution Approach 2:
The patent replaces the traditional mechanical approach of SSL decryption with a fingerprint-based feature extraction system. Instead of using dedicated hardware proxies to decrypt and analyze plaintext traffic, the system substitutes this with a software-based approach that extracts identifying features directly from encrypted TLS handshakes and traffic patterns
2Difficulty of detecting and measuring
If dedicated hardware proxies are deployed for SSL decryption and analysis, then attack traffic identification capability is improved, but security costs increase significantly
Solution Approach 1:
The patent creates a simplified copy or representation of attack traffic characteristics through fingerprint features. Instead of requiring expensive hardware to analyze the full encrypted traffic stream, the system extracts essential identifying features (fingerprints) that capture the unique characteristics of malicious tools, enabling detection through cheaper software-based matching
Solution Approach 2:
The patent replaces expensive, long-lived hardware proxy infrastructure with inexpensive, software-based fingerprint extraction and matching. The fingerprint database can be easily updated and replaced without requiring hardware changes, making the system more cost-effective and adaptable
3Difficulty of detecting and measuring
If machine learning models are used for HTTPS traffic analysis, then detection capability is improved, but false positive rate increases
Solution Approach 1:
The patent segments the detection process into distinct phases: extracting specific fingerprint features from TLS traffic, comparing these features against known malicious tool fingerprints in a database, and making identification decisions based on feature matching. This segmentation replaces the monolithic machine learning approach with a modular system that focuses on specific identifying characteristics, reducing false positives while maintaining detection capability
Data Source
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AI summary
Disclosed are a method and an apparatus for traffic identification. In an example of the present disclosure, the apparatus may extract target features from acquired Hypertext Transfer Protocol Secure, HTTPS, traffic based on positions of attack features in hacking attack traffic. The apparatus may identify the HTTPS traffic as hacking attack traffic in response to determining that the first genetic features corresponding to a hacking tool incorporated in a fingerprint blacklist database are matched with the target features. The apparatus may match the target features with second genetic features in a tool genetic database to obtain a matching result in response to determining that no first genetic features corresponding to hacking tools incorporated in a fingerprint blacklist database are matched with the target features, wherein the second genetic features in the tool genetic database are obtained by combining the first genetic features of each hacking tool in the fingerprint blacklist database; and identify the HTTPS traffic as hacking attack traffic in response to determining that the matching result meets a hacking identification condition.