Traffic Analysis of High Throughput Traffic on Tor

Mani, Ganapathy; Srinivas, P V V S; Rao, Ganga Rama Koteswara; Prasad, Chitturi; Priyanka, D Jadhav; Cherukuri, Naresh

doi:10.1109/i-smac52330.2021.9640934

Cited by 1 publication

(3 citation statements)

References 8 publications

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“…Traffic attacks Studies on new proposed traffic attacks [46], [37], [47] Relay detection Deanonymizing Tor traffic through relay detection [48], [49] Counterattack Counter attack techniques in the dark web [50], [51], [52] Real-time attack detection Applying attack detection techniques on real-time data [53], [54], [55] Data balancing Impact of data balancing Studies focusing on data balancing techniques and their impact on darknet traffic classification [56], [57] Feature selection Feature selection algorithm Studies focusing on the feature selection for the classification purpose [58], [57], [12], [59] Figure 9 Percentage Classification of the Literature reviewed…”

Section: Classification Of Applicationsmentioning

confidence: 99%

“…The user can choose the link for data transfer but cannot modify the composition of various nodes on the link. The classification of anonymous network traffic has become increasingly difficult due to the implementation of encryption technology [48].…”

Section: A Darknetmentioning

confidence: 99%

“…This is because the header information and content are encrypted before the packet enters the Tor network. Although it is possible to conduct this kind of traffic analysis both before and after the original packet enters and exits the Tor network, doing so is outside the purview of our threat model [48].…”

Section: B) Payload-based Techniquesmentioning

confidence: 99%

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Darknet Traffic Analysis: A Systematic Literature Review

Saleem,

Islam,

Islam

2024

IEEE Access

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The primary objective of an anonymity tool is to protect the anonymity of its users through the implementation of strong encryption and obfuscation techniques. As a result, it becomes very difficult to monitor and identify users' activities on these networks. Moreover, such systems have strong defensive mechanisms to protect users against potential risks, including the extraction of traffic characteristics and website fingerprinting. However, the strong anonymity feature also functions as a refuge for those involved in illicit activities who aim to avoid being traced on the network. As a result, a substantial body of research has been undertaken to examine and classify encrypted traffic using machine-learning techniques. This paper presents a comprehensive examination of the existing approaches utilized for the categorization of anonymous traffic as well as encrypted network traffic inside the darknet. Also, this paper presents a comprehensive analysis of methods of darknet traffic using ML (machine learning) techniques to monitor and identify the traffic attacks inside the darknet.

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