Observing Consistency in Online Communication Patterns for User Re-Identification

Ikuesan, Richard Adeyemi; Razak, Shukor Abd; Salleh, Mazleena; Venter, Hein S.

doi:10.1371/journal.pone.0166930

Cited by 19 publications

(15 citation statements)

References 14 publications

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“…Both information criteria favor the choice of a cross-sectional ARMA(1,1) model with one autoregressive and one moving-average term. 2 Notably, this model outperforms models with higher-order lag structures. This is advantageous in our case as it yields a parsimonious model specification and thus ensures interpretability.…”

Section: Model Selectionmentioning

confidence: 88%

“…Panel B reports the baseline variables that are used as part of the cohort-specific sensitivity analyses. 2 We perform a series of model checks. The model residuals are still subject to a remaining but slight serial correlation.…”

Section: Tablementioning

confidence: 99%

“…Third, the input to our model is given by univariate patient data. Thereby, we follow extensive research in the area of decision support that is based on univariate modeling (e.g., [2,6,12,14]). In our work, the univariate specification was intentionally chosen to maintain interpretability.…”

Section: Limitations and Generalizabilitymentioning

confidence: 99%

See 2 more Smart Citations

Longitudinal healthcare analytics for disease management: Empirical demonstration for low back pain

Mueller-Peltzer

Feuerriegel

Nielsen

et al. 2020

Decision Support Systems

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Clinician guidelines recommend health management to tailor the form of care to the expected course of diseases. Hence, in order to decide upon a suitable treatment plan, health professionals benefit from decision support, i.e., predictions about how a disease is to evolve. In clinical practice, such a prediction model requires interpretability. Interpretability, however, is often precluded by complex dynamic models that would be capable of capturing the intrapersonal variability of disease trajectories. Therefore, we develop a cross-sectional ARMA model that allows for inference of the expected course of symptoms. Distinct from traditional time series models, it generalizes to cross-sectional settings and thus patient cohorts (i.e., it is estimated to multiple instead of single disease trajectories). Our model is evaluated according to a longitudinal 52-week study involving 928 patients with low back pain. It achieves a favorable prediction performance while maintaining interpretability. In sum, we provide decision support by informing health professionals about whether symptoms will have the tendency to stabilize or continue to be severe.

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Section: Model Selectionmentioning

confidence: 88%

Section: Tablementioning

confidence: 99%

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Longitudinal healthcare analytics for disease management: Empirical demonstration for low back pain

Mueller-Peltzer

Feuerriegel

Nielsen

et al. 2020

Decision Support Systems

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show abstract

“…Research on user identification in areas of behavioral inference (Fan et al 2014;Yang 2010;Adeyemi et al 2014), biometric dynamics (Ernsberger et al 2017;Ikuesan and Venter 2018;Ikuesan et al 2019), and network traffic analysis (Li et al 2013a;Melnikov and Schönwälder 2010a;Adeyemi et al 2016) are methods adapted for user identification through pattern extraction. The process employed for network traffic pattern extraction includes logs and media scavenging, mining of audit trails, client-side caching, and extraction of flow records from captured network traffic.…”

Section: Introductionmentioning

confidence: 99%

“…A knowledge-based behavioral identifier system combines the uniqueness of user identifiers, user's unique pattern of usage, and other probable predefined characteristic behavior of users, to ascertain, to a degree of nearer certainty, the identity of a user. User profiling methods (Yang 2010;Adeyemi et al 2016) attempt to establish facts based on the assumed/ predefined salient features of the subject/object under observation. Similarly, such behavior-knowledge-based identifiers are applicable to network traffic profiling (Li et al 2013a;Hlavacs et al 1999).…”

Section: Introductionmentioning

confidence: 99%

A heuristics for HTTP traffic identification in measuring user dissimilarity

Ikuesan

Salleh

Venter

et al. 2020

Hum.-Intell. Syst. Integr.

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The prevalence of HTTP web traffic on the Internet has long transcended the layer 7 classification, to layers such as layer 5 of the OSI model stack. This coupled with the integration-diversity of other layers and application layer protocols has made identification of user-initiated HTTP web traffic complex, thus increasing user anonymity on the Internet. This study reveals that, with the current complex nature of Internet and HTTP traffic, browser complexity, dynamic web programming structure, the surge in network delay, and unstable user behavior in network interaction, user-initiated requests can be accurately determined. The study utilizes HTTP request method of GET filtering, to develop a heuristic algorithm to identify user-initiated requests. The algorithm was experimentally tested on a group of users, to ascertain the certainty of identifying user-initiated requests. The result demonstrates that user-initiated HTTP requests can be reliably identified with a recall rate at 0.94 and F-measure at 0.969. Additionally, this study extends the paradigm of user identification based on the intrinsic characteristics of users, exhibited in network traffic. The application of these research findings finds relevance in user identification for insider investigation, e-commerce, and e-learning system as well as in network planning and management. Further, the findings from the study are relevant in web usage mining, where user-initiated action comprises the fundamental unit of measurement.

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Polychronicity tendency-based online behavioral signature

Ikuesan

Razak

Venter

et al. 2017

Int. J. Mach. Learn. & Cyber.

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The proliferation of ubiquitous and pervasive computing devices has led to the emergence of research areas like Internet of things, and the Big-Data, which has seen a rise in obfuscation of online identity thus fueling an increase in online anonymity. Online anonymity constitutes a major platform for the exploitation of the potentials of cyber-crime; at the same time, it also inhibits the potential economic power that can be harnessed from the surging Internet population. Methods of online identification, such as usage profiling, demographic profiling, cookie-based identification process, media fingerprinting as well as token-based identification processes, are limited to either system identification or oneto-one identification. Current one-to-one identification mechanisms require huge volumes of templates of known users, and cannot be applied to novel users. This study proposed a psychosocial approach that integrates the human Polyphasia tendency into online identification processes for a one-to-many identification process. To achieve this, the study administered a Polychronic-Monochronic tendency scale measurement instrument to staff members of a research unit in a university, and the server-side network traffic of each respondent was monitored and collected. A logistic model tree was adapted for the one-to-many classification model based on human intrinsic features extracted from the network traffic and Polyphasia dichotomy. High degree of reliable accuracy of >80% was achieved which suggests a reliable model that supports the underlying hypothesis of the proposed model. Based on this accuracy, the approach finds practical relevance in online profiling process for online identification as well as online demographic profiling for e-commerce and e-learning. Furthermore, this approach can be applied to improve recommender systems in areas such as prediction and profile delivery through the extraction of the purpose of online surfing.

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Observing Consistency in Online Communication Patterns for User Re-Identification

Cited by 19 publications

References 14 publications

Longitudinal healthcare analytics for disease management: Empirical demonstration for low back pain

Longitudinal healthcare analytics for disease management: Empirical demonstration for low back pain

A heuristics for HTTP traffic identification in measuring user dissimilarity

Polychronicity tendency-based online behavioral signature

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