Traditional TCP/IP fingerprinting tools (e.g., nmap) are poorly suited for Internet-wide use due to the large amount of traffic and intrusive nature of the probes. This can be overcome by approaches that rely on a single SYN packet to elicit a vector of features from the remote server; however, these methods face difficult classification problems due to the high volatility of the features and severely limited amounts of information contained therein. Since these techniques have not been studied before, we first pioneer stochastic theory of single-packet OS fingerprinting, build a database of 116 OSes, design a classifier based on our models, evaluate its accuracy in simulations, and then perform OS classification of 37.8M hosts from an Internet-wide scan.
Traditional TCP/IP fingerprinting tools (e.g., nmap) are poorly suited for Internet-wide use due to the large amount of traffic and intrusive nature of the probes. This can be overcome by approaches that rely on a single SYN packet to elicit a vector of features from the remote server; however, these methods face difficult classification problems due to the high volatility of the features and severely limited amounts of information contained therein. Since these techniques have not been studied before, we first pioneer stochastic theory of single-packet OS fingerprinting, build a database of 116 OSes, design a classifier based on our models, evaluate its accuracy in simulations, and then perform OS classification of 37.8M hosts from an Internet-wide scan.
Traditional TCP/IP fingerprinting tools (e.g., nmap) are poorly suited for Internet-wide use due to the large amount of traffic and intrusive nature of the probes. This can be overcome by approaches that rely on a single SYN packet to elicit a vector of features from the remote server; however, these methods face difficult classification problems due to the high volatility of the features and severely limited amounts of information contained therein. Since these techniques have not been studied before, we first pioneer stochastic theory of single-packet OS fingerprinting, build a database of 116 OSes, design a classifier based on our models, evaluate its accuracy in simulations, and then perform OS classification of 37.8M hosts from an Internet-wide scan.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.