Remote Operation System Detection Base on Machine Learning

Author

Zhang, Bofeng ; Zou, Tiezheng ; Wang, Yongjun ; Zhang, Baokang

Author_Institution

Comput. Sch., Nat. Univ. of Defense Technol., Changsha, China

fYear

2009

fDate

17-19 Dec. 2009

Firstpage

539

Lastpage

542

Abstract

A machine learning method for remote operation system recognition through their detection signatures with support vector machine (SNM) is proposed. A vector space model of Nmap fingerprint database and techniques for translating the host responses to SVM input vectors are also suggested. Experimental result on identification of signatures in the fingerprint database of Nmap 4.90RC1 but not known for Nmap 4.76 show that our method is effective in the discovery of new signatures not included in current fingerprint database.

Keywords

digital signatures; learning (artificial intelligence); operating systems (computers); support vector machines; Nmap 4.76; Nmap 4.90RC1; Nmap fingerprint database; SVM input vectors; detection signatures; machine learning; remote operation system detection; signature identification; support vector machine; vector space model; Computer science; Databases; Educational institutions; Fingerprint recognition; Learning systems; Machine learning; Protocols; Space technology; Support vector machine classification; Support vector machines; OS detection; OS fingerprint; machine learning; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Frontier of Computer Science and Technology, 2009. FCST '09. Fourth International Conference on

Conference_Location

Shanghai

Print_ISBN

978-0-7695-3932-4

Electronic_ISBN

978-1-4244-5467-9

Type

conf

DOI

10.1109/FCST.2009.21

Filename

5392866