PENGENALAN POLA TRAFIK DATA TOR BROWSER MENGGUNAKAN CLUSTERING K-MEANS

SAPUTRA, DONI and Stiawan, Deris and Heryanto, Ahmad (2020) PENGENALAN POLA TRAFIK DATA TOR BROWSER MENGGUNAKAN CLUSTERING K-MEANS. Undergraduate thesis, Sriwijaya University.

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Abstract

Nowadays anonymity and privacy, there are several types and implementations of anonymous services available on the Internet. Tor is one of those services. Activists, journalists and writers use this tool for free speech, but it is also used incorrectly. for example illegal hacks and attacks, spreading malware or scams, pornographic sites, narcotics and other illegal transactions, and many more. The purpose of this research is to extract traffic packets on the ISCX Dataset, train data to find the attributes of the data packets used in pattern recognition, recognize patterns manually and apply the K-Means Clustering method to recognize ISCX Dataset. This study uses a dataset from ISCX, the tools used in retrieving the ISCX Dataset are Whonix (https://www.whonix.org), a Linux OS that is ready to use and configured to route all traffic through the Tor network. The process of analyzing and data acquisition using the Wireshark tool aims for the purposes of the pattern recognition process in the form of data attributes or information about the Log Activity and Protocols of the TOR Dataset traffic. In this study, it was successful in recognizing the pattern of the TOR Dataset, both manually and with the application of K-Means Clustering, namely the attributes of the tor and normal traffic protocols with the ratio of the number of TLS protocol traffic tor greater than normal traffic.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	TOR (The Onion Router) Network, TLS (Transport Layer Security), Clustering K-Means.
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.A25 Computer security. Systems and Data Security.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Users 8014 not found.
Date Deposited:	11 Jan 2021 07:25
Last Modified:	11 Jan 2021 07:25
URI:	http://repository.unsri.ac.id/id/eprint/39585

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