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Erschienen in:
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2022 | OriginalPaper | Buchkapitel

A Tool to Extract Onion Links from Tor Hidden Services and Identify Illegal Activities

verfasst von : Varun Nair, Jinesh M. Kannimoola

Erschienen in: Inventive Computation and Information Technologies

Verlag: Springer Nature Singapore

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Abstract

The dark web is a covered segment of the Internet that provides privacy-protected network access. Tor is a volunteer run prominent dark web network that becomes heaven for criminals to conduct illegal activities. The use of multilayer encryption to achieve anonymity poses a significant hurdle for the law enforcement agency to monitor illicit activities inside the hidden Network. Our study investigates an alternative method to extract the hidden service descriptor from the network. These descriptors also called onion links open a door to hidden services inside dark web. We use a flaw in the v2 protocol to collect the address of hidden service from the memory of a Tor Hidden Service Directory. Automated data extraction and analyzes module provide more insight into contents propagating in Tor network. Using our experiment setup, 4000 onion links are collected and examined. Our analysis shows that socially unjust materials form significant portions of the Tor network.

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Vorheriges Kapitel Hardware Trojan Detection at Behavioral Level Using Inline Assertions and Verification Using UVM
Nächstes Kapitel Adaptive IoT System for Precision Agriculture
Literatur
1.
H. Al Jawaheri, M. Al Sabah, Y. Boshmaf, A. Erbad, Deanonymizing tor hidden service users through bitcoin transactions analysis. Comput. Secur. 89, 101684 (2020)CrossRef
2.
M.W. Al Nabki, E. Fidalgo, E. Alegre, I. de Paz, Classifying illegal activities on tor network based on web textual contents, in Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, vol. 1, Long Papers. pp. 35–43 (2017)
3.
M. AlSabah, K. Bauer, I. Goldberg, Enhancing tor’s performance using real-time traffic classification, in Proceedings of the 2012 ACM Conference on Computer and Communications Security, pp. 73–84 (2012)
4.
G. Ballarotto, B. Volpi, E. Marzilli, R. Tambelli, Adolescent internet abuse: a study on the role of attachment to parents and peers in a large community sample. BioMed Res. Int. 2018 (2018)
5.
J. Barker, P. Hannay, P. Szewczyk, Using traffic analysis to identify the second generation onion router, in 2011 IFIP 9th International Conference on Embedded and Ubiquitous Computing (IEEE, 2011), pp. 72–78
6.
A. Biryukov, I. Pustogarov, R.P. Weinmann, Trawling for tor hidden services: detection, measurement, deanonymization, in 2013 IEEE Symposium on Security and Privacy (IEEE, 2013), pp. 80–94
7.
S. Chakravarty, A. Stavrou, A.D. Keromytis, Identifying proxy nodes in a tor anonymization circuit, in 2008 IEEE International Conference on Signal Image Technology and Internet Based Systems (IEEE, 2008), pp. 633–639
8.
C. Iliou, G. Kalpakis, T. Tsikrika, S. Vrochidis, I. Kompatsiaris, Hybrid focused crawling on the surface and the dark web. EURASIP J. Inf. Secur. 2017(1), 1–13 (2017)
9.
S. Jeziorowski, M. Ismail, A. Siraj, Towards image-based dark vendor profiling: an analysis of image metadata and image hashing in dark web marketplaces, in Proceedings of the Sixth International Workshop on Security and Privacy Analytics, pp. 15–22 (2020)
10.
G. Kadianakis, C.V. Roberts, L.M. Roberts, P. Winter, Major key alert anomalous keys in tor relays, in International Conference on Financial Cryptography and Data Security (Springer, 2018), pp. 3–19
11.
E. Kermitsis, D. Kavallieros, D. Myttas, E. Lissaris, G. Giataganas, Dark web markets, in Dark Web Investigation (Springer, 2021), pp. 85–118
12.
Z. Ling, J. Luo, W. Yu, X. Fu, W. Jia, W. Zhao, Protocol-level attacks against tor. Comput. Netw. 57(4), 869–886 (2013)CrossRef
13.
N. Mannilthodi, J.M. Kannimoola, Secure IoT: an improbable reality, in IoTBDS, pp. 338–343 (2017)
14.
J. Marques, L. Velasco, R. van Duijn, Tor: Hidden Service Intelligence Extraction (2018)
15.
B. Monk, J. Mitchell, R. Frank, G. Davies, Uncovering tor: a examination of the network structure. Secur. Commun. Netw. 2018 (2018)
16.
S.J. Murdoch, Hot or not: revealing hidden services by their clock skew, in Proceedings of the 13th ACM Conference on Computer and Communications Security, pp. 27–36 (2006)
17.
P. Narayanan, R. Ani, A.T. King, Torbot: open source intelligence tool for dark web, in Inventive Communication and Computational Technologies (Springer, 2020), pp. 187–195
18.
19.
20.
M. Rm, D. Radha, A comprehensive approach for network security, in 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT) (IEEE, 2018), pp. 420–426
21.
S. Saleh, J. Qadir, M.U. Ilyas, Shedding light on the dark corners of the internet: a survey of tor research. J. Netw. Comput. Appl. 114, 1–28 (2018)CrossRef
22.
M.A. Sulaiman, S. Zhioua, Attacking tor through unpopular ports, in 2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops (IEEE, 2013), pp. 33–38
23.
P. Syverson, G. Tsudik, M. Reed, C. Landwehr, Towards an analysis of onion routing security, in Designing Privacy Enhancing Technologies (Springer, 2001), pp. 96–114
24.
25.
R. Vinayakumar, K. Soman, P. Poornachandran, S. Akarsh, Application of deep learning architectures for cyber security, in Cybersecurity and Secure Information Systems (Springer, 2019), pp. 125–160
26.
P. Winter, A. Edmundson, L.M. Roberts, A. Dutkowska-Żuk, M. Chetty, N. Feamster, How do tor users interact with onion services? in 27th \(\{\)USENIX\(\}\) Security Symposium (\(\{\)USENIX\(\}\) Security 18), pp. 411–428 (2018)
Metadaten
Titel
A Tool to Extract Onion Links from Tor Hidden Services and Identify Illegal Activities
verfasst von
Varun Nair
Jinesh M. Kannimoola
Copyright-Jahr
2022
Verlag
Springer Nature Singapore
Buch
Inventive Computation and Information Technologies

Print ISBN: 978-981-16-6722-0

Electronic ISBN: 978-981-16-6723-7

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-981-16-6723-7_3

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