Demographics inference through Wi-Fi network traffic analysis

Li, Huaxin; Xu, Zheyu; Zhu, Haojin; Ma, Di; Li, Shuai; Xing, Kai

doi:10.1109/infocom.2016.7524528

Cited by 36 publications

(17 citation statements)

References 17 publications

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“…Internet user behavior [2,[6][7][8][9][10][11][12] has long been talked about and researched. Major aspects of this area include behavior modeling in the cloud [7,8], in online social networks (OSNs) [9,10], in network traffic [2,6] and so forth. Ning Xia [2] developed an integrated framework to quantify user behaviors, which leverages information from both OSN pages and traffic.…”

Section: Related Workmentioning

confidence: 99%

“…Traffic-analysis-based user classification extracts user information from the internet traffic, and it builds detailed user profiles for classification [1][2][3][4][5][6]. Thomas Karagiannis [1] built a graphlet-based method to quantify user communication behaviors for user profiling and classification.…”

Section: Related Workmentioning

confidence: 99%

“…Flow connection based methods [1] are light-weighted, and their features are relatively easy to calculate. Statistical methods [2][3][4][5][6] outstand in scalability, and they are more versatile in the choice of features.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Argus: Traffic Behavior Based Prediction of Internet User Demographics through Hierarchical Neural Network

Wang

Liu

et al. 2020

Electronics

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Predicting internet user demographics based on traffic behavior analysis can provide effective clues for the decision making of network administrators. Nonetheless, most of the existing researches overly rely on hand-crafted features, and they also suffer from the shallowness of information mining and the limitation in prediction targets. This paper proposes Argus, a hierarchical neural network solution to the prediction of Internet user demographics through traffic analysis. Argus is a hierarchical neural-network structure composed of an autoencoder for embedding and a fully-connected net for prediction. In the embedding layer, the high-level features of the input data are learned, with a customized regularization method to enforce their discriminative power. In the classification layer, the embeddings are converted into the label predictions of the sample. An integrated loss function is provided to Argus for end-to-end learning and architecture control. Argus has exhibited promising performances in experiments based on real-world dataset, where most of the metrics outperform those achieved by common machine learning techniques on multiple prediction targets. Further experiments reveal that the integrated loss function is capable of promoting Argus performance, and the contribution of a specific loss component during the training process is validated. Empirical settings for hyper parameters are given according to the experiments.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Argus: Traffic Behavior Based Prediction of Internet User Demographics through Hierarchical Neural Network

Wang

Liu

et al. 2020

Electronics

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“…Much research on demographic prediction focuses on cyber behaviours investigate through Web pages browsed [2][3][4][5], queries issued [2,6], mobile Apps installed, or Web contributed or commented upon (e.g., images, tweets, comments, likes) [7][8][9].…”

Section: Cyber Behaviormentioning

confidence: 99%

“…The researchers found that browsing histories are a strong signal for inferring demographics. Li et al [5] predicted user demographics from both unencrypted and encrypted Web traffic. They found that reasonable accuracy could be obtained predicting gender and education level.…”

Section: Cyber Behaviormentioning

confidence: 99%

Understanding the predictability of user demographics from cyber-physical-social behaviours in indoor retail spaces

et al. 2018

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Understanding the association between customer demographics and behaviour is critical for operators of indoor retail spaces. This study explores such an association based on a combined understanding of customer Cyber (online), Physical, and (some aspects of ) Social (CPS) behaviour, at the conjunction of corresponding CPS spaces. We combine the results of a traditional questionnaire with large-scale WiFi access logs, which capture customer cyber and physical behaviour. We investigate the predictability of user demographics based on CPS behaviors captured from both sources. We find (1) strong correlations between users' demographics and their CPS behaviors; (2) log-recorded cyber-physical behavior reflects well data captured in the corresponding questionnaire; (3) different CPS behaviors contribute differently to the predictability of demographic attributes; and (4) the predictability of user demographics from logs is comparable to questionnaire-based data. As such, our study provides strong support for demographic studies based on large-scale logs data capture.

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Who Leaks My Privacy: Towards Automatic and Association Detection with GDPR Compliance

Jia

Zhou

et al. 2019

Lecture Notes in Computer Science

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Demographics inference through Wi-Fi network traffic analysis

Cited by 36 publications

References 17 publications

Argus: Traffic Behavior Based Prediction of Internet User Demographics through Hierarchical Neural Network

Argus: Traffic Behavior Based Prediction of Internet User Demographics through Hierarchical Neural Network

Understanding the predictability of user demographics from cyber-physical-social behaviours in indoor retail spaces

Who Leaks My Privacy: Towards Automatic and Association Detection with GDPR Compliance

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