Fine-Grained High-Utility Dynamic Fingerprinting Extraction for Network Traffic Analysis

Previous network feature extraction methods used for network anomaly detection have some problems, such as being unable to extract features from the original network traffic, or that they can only extract coarse-grained features, as well as that they are highly dependent on manual analysis. To solve these problems, this paper proposes a fine-grained and highly practical dynamic application fingerprint extraction method. By putting forward a fine-grained high-utility dynamic fingerprinting (Huf) algorithm to build a Huf-Tree based on the N-gram (every substring of a larger string, of a fixed length n) model, combining it with the network traffic segment-IP address transition (IAT) method to achieve dynamic application fingerprint extraction, and through the utility of fingerprint, the calculation was performed to obtain a more valuable fingerprint, to achieve fine-grained and efficient flow characteristic extraction, and to solve the problem of this method being highly dependent on manual analysis. The experimental results show that the Huf algorithm can realize the dynamic application of fingerprint extraction and solve the existing problems.