Abstract
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 one-to-one identification. Current one-to-one identification mechanisms require huge volume of templates of known users, and cannot be applied to novel users. This study proposed a psychosocial approach that integrates the composition of 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 in eight-months duration. A logistic model tree—after an initial classifier exploration process—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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References
Miniwatts MG (2015) Internet usage statistics: the internet big picture. World Internet Users and 2015 Population Stats [Online]. http://www.internetworldstats.com/stats.htm. Accessed 04 Aug 2015
Tor-Project (2015) Tor: anonymity online [Online]. https://www.torproject.org/index.html.en. Accessed: 04 Aug 2015
Padmanabhan B, Yang Y (2007) Clickprints on the web: are there signatures in web browsing data? Available SSRN http://ssrn.com/abstract=931057 or https://doi.org/10.2139/ssrn.931057
Yang (Catherine) Y (2010) Web user behavioral profiling for user identification. Decis Support Syst 49(3):261–271, Jun.
Yang Y, Padmanabhan B (2010) Toward user patterns for online security: Observation time and online user identification. Decis Support Syst 48(4):548–558
Herrmann D, Gerber C, Banse C, Federrath H, Espoo (2010) Analyzing characteristic host access patterns for re-identification of web user sessions, ISBN: 978-., vol 7127 LNCS. Springer, Finland
Abramson M, Aha D (2013) User authentication from web browsing behavior. In: The Twenty-Sixth International FLAIRS Conference, pp 268–273
Baggili I, Rogers M (2009) Self-reported cyber crime: an analysis on the effects of anonymity and pre-employment integrity. Int J Cyber Criminol 3(2):536–549
Brown B, Lampe C, Rodden K, Ducheneaut N (2010) Models, theories and methods of studying online behaviour. In: CHI’10 extended abstracts on human factors in computing systems, pp 4449–4452
Amichai-Hamburger Y, Wainapel G, Fox S (2002) ‘On the Internet no one knows I’m an introvert’: extroversion, neuroticism, and Internet interaction. Cyberpsychol Behav 5(2):125–128
Zhang Y, Goonetilleke RSR, Plocher T, Liang SSMSSM (2004) Time orientation and human performance. Hong Kong Univ Sci Technol Kowloon 1999:247–252
Rose GM, Evaristo R, Straub D (2003) Culture and consumer responses to web download time: a four-continent study of mono and polychronism. IEEE Trans Eng Manag 50(1):31–44
Palmer DK, Schoorman FD (1999) Unpackaging the multiple aspects of time in polychronicity. J Manag Psychol 14(3/4):323–345
Lee W, Tan T, Hameed S (2006) Polychronicity, the internet, and the mass media: a Singapore study. J Comput Mediat Commun 11:300–316
Kralisch A, Eisend M, Berendt B (2005) The impact of culture on website navigation behaviour. In: The 11th International Conference on Human–Computer Interaction (HCI International), pp 1–9
Joshua D, Lee MLS, Yi MY (2009) Time-user preference and technology acceptance: Measure development of computer polychronicity. Am J Bus 24(2):23–32
Capdeferro N, Romero M, Barberà E (2014) Polychronicity: review of the literature and a new configuration for the study of this hidden dimension of online learning. Distance Educ 35(3):294–310
Lindquist JD, Kaufman-Scarborough C (2007) The Polychronic monochronic tendency model: PMTS scale development and validation. Time Soc 16(2–3):253–285
Kumar R, Tomkins A (2010) A characterization of online browsing behavior. In: Proceedings of the 19th international conference World Wide Web, pp 561–570
Adeyemi IR, Razak AS, Salleh M (2014) A psychographic framework for online user identification. In: International Symposium on Biometrics and Security Technologies (ISBAST), pp 198–203
Senin P, Lin J, Wang X, Oates T (2014) GrammarViz 2.0: a tool for grammar-based pattern discovery in time series. In: The European conference on machine learning and principles and practice of knowledge discovery in databases, pp 468–472
Barabasi A (2005) The origin of bursts and heavy tails in human dynamics. Nature 435(7039):207–211
Zhou T, Han X, Wang B (2008) Towards the Understanding of Human Dynamics. Sci Matters Humanit Complex Syst 1:207–233
Nguyen TTT, Armitage G (2008) A survey of techniques for internet traffic classification using machine learning. Commun Surv Tutorials 10(4):56–76 IEEE
Kotsiantis S, Zaharakis I, Pintelas P (2007) Supervised machine learning: a review of classification techniques. Informatical, vol 31, pp 249–268
Othman FM, Moh T, Yau S (2007) Comparison of different classification techniques using WEKA for Breast cancer. In: 3rd Kuala Lumpur International Conference on Biomedical Engineering, vol 15, pp 520–523
Yang YC (2010) Web user behavioral profiling for user identification. Decis Support Syst 49(3):261–271
Adeyemi IR, Razak SA, Salleh M, Venter HS (2016) Observing consistency in online communication patterns for user re-identification. PLoS One 11(12):1–27
Bauer E, Kohavi R, Chan P, Stolfo S, Wolpert D (1999) An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Mach Learn 36:105–139
Dietterich TG (2000) An experimental Comparison of three methods for constructing ensembles of decision Trees. Mach Learn 40:139–157
Adeyemi IR, Razak SA, Venter HS, Salleh M (2017) High-level online user attribution model based on human Polychronic-Monochronic tendency. In: 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017, pp 445–450
Li X, Xie H, Chen L, Wang J, Deng X (2014) News impact on stock price return via sentiment analysis. Knowl Based Syst 69(1):14–23
Cambria E, Speer R, Havasi C, Hussain A (2010) SenticNet : a publicly available semantic resource for opinion mining. Artif Intell 10:14–18
Gu B, Sun X, Sheng VS (2017) Structural minimax probability machine. IEEE Trans Neural Netw Learn Syst 28(7):1646–1656
Acknowledgements
We would like to thank the Ministry of Education, Malaysia for sponsoring this research grant (Vote: R.J130000.7813.4F193), Universiti Teknologi Malaysia and University of Pretoria. The preliminary version of this article has been published in ASC 2017 in conjunction with BIGCOMP 2017 [31].
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Ikuesan, A.R., Razak, S.A., Venter, H.S. et al. Polychronicity tendency-based online behavioral signature. Int. J. Mach. Learn. & Cyber. 10, 2103–2118 (2019). https://doi.org/10.1007/s13042-017-0748-7
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DOI: https://doi.org/10.1007/s13042-017-0748-7