Moreover, it can be used to perform DDoS (Distributed Denial of Service) attacks or to click on referral links, thus creating revenue.
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It is utilized by malware, e.g., to connect to the C&C server to register or obtain commands, check the infected machine’s IP address, or download additional modules. , HTTP is the most common protocol used in the Command and Control (C&C) traffic, more popular than Hypertext Transfer Protocol Secure (HTTPS).
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As a result, Hfinger can effectively track and hunt malware by providing more unique fingerprints than other standard tools.Ĭurrently, malicious software (malware) developers use Hypertext Transfer Protocol (HTTP) as one of the primary carriers for malicious communication. Moreover, unlike these tools, in default mode, Hfinger does not introduce collisions between malware and benign applications and achieves it by increasing the number of fingerprints by at most 3 times.
The conducted effectiveness analysis reveals that on average only 1.85% of requests fingerprinted by Hfinger collide between malware families, what is 8–34 times lower than existing tools. For the developed solution, we have performed an extensive experimental evaluation using real-world data sets and we also compared Hfinger with the most related and popular existing tools such as FATT, Mercury, and p0f. It extracts information from the parts of the request such as URI, protocol information, headers, and payload, providing a concise request representation that preserves the extracted information in a form interpretable by a human analyst. To address these issues, we propose Hfinger, a novel malware HTTP request fingerprinting tool. However, currently existing tools do not analyze all information included in the HTTP message or analyze it insufficiently. To this aim, fingerprinting tools have been developed to help track and identify such traffic by providing a short representation of malicious HTTP requests. Malicious software utilizes HTTP protocol for communication purposes, creating network traffic that is hard to identify as it blends into the traffic generated by benign applications.
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