Googling the Internet: Profiling Internet Endpoints via the World Wide Web

Trestian, Ionut; Ranjan, Supranamaya; Kuzmanovic, Aleksandar; Nucci, Antonio

doi:10.1109/tnet.2009.2031175

Cited by 33 publications

(20 citation statements)

References 11 publications

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“…traffic cluster) levels. Specifically, traffic classes include: (i) traffic cluster such as bulk or small transactions; (ii) application type such as game, browsing or chat; (iii) application protocol such as hypertext transfer protocol (HTTP), HTTP secure (HTTPS), file transfer protocol (FTP), domain name system (DNS), simple mail transfer protocol (SMTP), post office protocol (POP3), secure sockets layer (SSL) or secure shell (SSH); (iv) application software such as a specific FTP or BitTorrent client software; (v) fine‐grained traffic classification such as Facebook chat, Google search or Skype voice call; and anomaly class.…”

Section: A Multilateral Taxonomy Of Traffic Classification Methodsmentioning

confidence: 99%

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A multilevel taxonomy and requirements for an optimal traffic‐classification model

2014

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Identifying Internet traffic applications is essential for network security and management. The steady emergence of new Internet applications, together with the use of encryption and obfuscation techniques, ensures that traffic classification remains a hot research topic. Much research has been devoted to this topic by the research community in the last decade. However, an optimal traffic classification model has yet to be defined. Many techniques and formats have been described, with the current literature therefore lacking appropriate benchmarks expressed in a consistent terminology. Moreover, existing surveys are outdated and do not include many recent advances in the field. In this article, we present a systematic multilevel taxonomy that covers a broad range of existing and recently proposed methods, together with examples of vendor classification techniques. Our taxonomy assists in defining a consistent terminology. It could be useful in future benchmarking contexts by characterizing and comparing methods at three different levels. From this perspective, we describe key features and provide design hints for future classification models, while emphasizing the main requirements for promoting future research efforts. To motivate researchers and other interested parties, we collect and share data captured from real traffic, using two models to protect data privacy.

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Section: A Multilateral Taxonomy Of Traffic Classification Methodsmentioning

confidence: 99%

“…Hybrid techniques received some attention with different combination approaches. Moreover, the literature shows few miscellaneous and graphical techniques that were less tracked.…”

Section: A Multilateral Taxonomy Of Traffic Classification Methodsmentioning

confidence: 99%

A multilevel taxonomy and requirements for an optimal traffic‐classification model

2014

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“…Other possible approaches can rely on developing well-crafted malicious third party applications, or using traffic sniffing tools when in presence of IEEE 802.11 unencrypted accesses. The latter can also allow to acquire information for forcing a target device via endpoint profiling through web search engines, such as Google (Trestian et al, 2010).…”

Section: Minimal Taxonomy-based Space Modellingmentioning

confidence: 99%

A taxonomy-based model of security and privacy in online social networks

Caviglione

Coccoli

Merlo

2014

IJCSE

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Social environments were already present in the original web vision, but nowadays are mainly available through online social networks (OSNs), which are a real cultural phenomenon. However, their actual deployment is very heterogeneous, reflecting into different development choices and functional architectures. Such aspects, jointly with the intrinsic sharing of personal information, lead to severe risks both in terms of security and privacy. In this perspective, our work proposes a taxonomy-based approach to describe and model the complex security space characterising OSNs. The contributions of this paper are: 1) to introduce a systematic approach to define the 'problem space' of an OSN; 2) to showcase basic models for organising the engineering and the needed checking procedures.Reference to this paper should be made as follows: Caviglione, L., Coccoli, M. and Merlo, A. (2014) 'A taxonomy-based model of security and privacy in online social networks', Int. J.

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“…Using data from a CSP, Keralapura et al [12] showed that there exist distinct behavior patterns among mobile users. Trestian et al [29] characterized the relationship between user application interests and their mobility properties. On a large scale, there have been studies characterizing mobile traffic and user interactive behaviors on embedded applications with smart phones [31].…”

Section: Related Workmentioning

confidence: 99%

Mosaic

Ning

Song²,

Liao³

et al. 2013

Proceedings of the ACM SIGCOMM 2013 Conference on SIGCOMM

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With the proliferation of online social networking (OSN) and mobile devices, preserving user privacy has become a great challenge. While prior studies have directly focused on OSN services, we call attention to the privacy leakage in mobile network data. This concern is motivated by two factors. First, the prevalence of OSN usage leaves identifiable digital footprints that can be traced back to users in the real-world. Second, the association between users and their mobile devices makes it easier to associate traffic to its owners. These pose a serious threat to user privacy as they enable an adversary to attribute significant portions of data traffic including the ones with NO identity leaks to network users' true identities. To demonstrate its feasibility, we develop the Tessellation methodology. By applying Tessellation on traffic from a cellular service provider (CSP), we show that up to 50% of the traffic can be attributed to the names of users. In addition to revealing the user identity, the reconstructed profile, dubbed as "mosaic," associates personal information such as political views, browsing habits, and favorite apps to the users. We conclude by discussing approaches for preventing and mitigating the alarming leakage of sensitive user information.

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Googling the Internet: Profiling Internet Endpoints via the World Wide Web

Cited by 33 publications

References 11 publications

A multilevel taxonomy and requirements for an optimal traffic‐classification model

A multilevel taxonomy and requirements for an optimal traffic‐classification model

A taxonomy-based model of security and privacy in online social networks

Mosaic

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