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dc.contributor.authorBhattasali, Tapalinaspa
dc.contributor.authorSaeed, Khalidspa
dc.date.accessioned2021-11-03T14:23:36Z
dc.date.available2021-11-03T14:23:36Z
dc.date.issued2021-09-17
dc.identifier.issn03029743spa
dc.identifier.urihttps://hdl.handle.net/11323/8831spa
dc.description.abstractNowadays, interaction with fake profiles of a genuine user in social media is a common problem. General users may not easily identify profiles created by fake users. Although various research works are going on all over the world to detect fake profiles in social media, focus of this paper is to remove additional efforts in detection procedure. Behavioral biometrics like typing pattern of users can be considered to classify genuine profile and fake profile without disrupting normal activities of the users. In this paper, DEEP_ID model is designed to detect fake profiles in Facebook like social media considering typing patterns like keystroke, mouse-click, and touch stroke. Proposed model can silently detect the profiles created by fake users when they type or click in social media from desktop, laptop, or touch devices. DEEP_ID model can also identify whether genuine profiles have been hacked by fake users or not in the middle of the session. The objective of proposed work is to demonstrate the hypothesis that user recognition algorithms applied to raw data can perform better if requirement for feature extraction can be avoided, which in turn can remove the problem of inappropriate attribute selection. Proposed DEEP_ID model is based on multi-view deep neural network, where network layers can learn data representation for user recognition based on raw data of typing pattern without feature selection and extraction. Proposed DEEP_ID model has achieved better results compared to traditional machine learning classifiers. It provides strong evidence that the stated hypothesis is valid. Evaluation results indicate that Deep_ID model is highly accurate in profile detection and efficient enough to perform fast detection.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoeng
dc.publisherComputer Information Systems and Industrial Managementspa
dc.rightsCC0 1.0 Universalspa
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/spa
dc.sourceInternational Conference on Computer Information Systems and Industrial Management CISIM 2021spa
dc.subjecttyping patternspa
dc.subjectkeystrokespa
dc.subjectmouse clickspa
dc.subjecttouch strokespa
dc.subjectfake profilespa
dc.subjectdeep_IDspa
dc.subjectsocial mediaspa
dc.titleTyping pattern analysis for fake profile detection in social Mediaspa
dc.typeCapítulo - Parte de Librospa
dc.source.urlhttps://link.springer.com/chapter/10.1007/978-3-030-84340-3_2spa
dc.rights.accessrightsinfo:eu-repo/semantics/closedAccessspa
dc.identifier.doi10.1007/978-3-030-84340-3_2spa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
dc.relation.referencesHinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)spa
dc.relation.referencesCruz, M.A.D.S., Goldschmidt, R.R.: Deep neural networks applied to user recognition based on keystroke dynamics: learning from raw data. In: Proceedings of the XV Brazilian Symposium on Information Systems. Article No.: 35, pp. 1–8 (2019)spa
dc.relation.referencesSun, L., Wang, Y., Cao, B., Yu, P.S., Srisa-an, W., Leow, A.D.: Sequential keystroke behavioral biometrics for mobile user identification via multi-view deep learning. In: Altun, Y., Das, K., Mielikäinen, T., Malerba, D., Stefanowski, J., Read, J., Žitnik, M., Ceci, M., Džeroski, S. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10536, pp. 228–240. Springer, Cham (2017). https://ezproxy.cuc.edu.co:2067/10.1007/978-3-319-71273-4_19spa
dc.relation.referencesZhong, Y., Deng, Y.: A survey on keystroke dynamics biometrics: approaches, advances, and evaluations. In: Recent Advances in User Authentication Using Keystroke Dynamics Biometrics, pp. 1–22 (2015)spa
dc.relation.referencesBaynath, P., Soyjaudah, K.M S., Khan, M.H-M.: Implementation of a secure keystroke dynamics using ant colony optimization. In: Proceedings of International Conference on Communications, Computer Science and Information Technology (2016)spa
dc.relation.referencesLiu, F., Deng, Y.: Determine the number of unknown targets in open world based on elbow method. IEEE Trans. Fuzzy Syst. 29(5), 986–995 (2021)spa
dc.relation.referencesLever, J., Krzywinski, M., Altman, N.: Points of Significance: principal component analysis. Nat. Methods 14(7), 641–642 (2017)spa
dc.relation.referencesSherstinsky, A.: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D 404, 132306 (2020)spa
dc.relation.referencesHochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)spa
dc.relation.referencesDu, Y., Wang, W., Wang, L.: Hierarchical recurrent neural network for skeleton based action recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1110–1118 (2015)spa
dc.relation.referencesObaidat, M.S., Sadoun, B.: Verification of computer users using keystroke dynamics. IEEE Trans. Syst. Man Cybern. B Cybern. 27(2), 261–269 (1997)spa
dc.relation.referencesZhao, X., Feng, T., Shi, W.: Continuous mobile authentication using a novel graphic touch gesture feature. In: Proceedings of IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1–6 (2013)spa
dc.relation.referencesAbramson, M., Gore, S.: Associative patterns of web browsing behavior. In: AAAIFall Symposium Series (2013)spa
dc.relation.referencesZhang, H., Yan, Z., Yang, J., Tapia, E.M., Crandall, D.J.: Mfingerprint: privacy-preserving user modeling with multimodal mobile device footprints. In: Proceedings of International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, pp. 195–203. Springer, Cham (2014). https://ezproxy.cuc.edu.co:2067/10.1007/978-3-319-05579-4_24spa
dc.relation.referencesAhmed, A.A., Traore, I.: Biometric recognition based on free-text keystroke dynamics. IEEE Trans, Cybern. 44(4), 458–472 (2014)spa
dc.relation.referencesKillourhy, K.S., Maxion, R.A.: Comparing anomaly-detection algorithms for keystroke dynamics. In: Proceedings of IEEE/IFIP International Conference Dependable Systems & Networks, pp. 125–134 (2009)spa
dc.relation.referencesKillourhy, K.S., Kevin, S., Maxion, R.A., Roy, A.: Free vs. transcribed text for keystroke-dynamics evaluations. In: Proceedings of Workshop: Learning from Authoritative Security Experiment Results, pp. 1–8 (2012)spa
dc.relation.referencesBhattasali, T., Saeed, K.: Two factor remote authentication in healthcare. In: Proceedings of IEEE International Conference on Advances in Computing, Communications and Informatics, pp. 380–381 (2014)spa
dc.relation.referencesBhattasali, T., Saeed, K., Chaki, N., Chaki, R.: Bio-authentication for layered remote health monitor framework. J. Med. Inform. Technol. 23, 131–140 (2014)spa
dc.relation.referencesMaxion, R., Killourhy, K.: Keystroke biometrics with number-pad input. In: Proceedings of IEEE International Conference on Dependable Systems & Networks, pp. 201–210 (2010)spa
dc.relation.referencesXu, H., Zhou, Y., Lyu, M.R.: Towards continuous and passive authentication via touch biometrics: an experimental study on smartphones. In: Proceedings of Symposium on Usable Privacy and Security, pp. 187–198 (2014)spa
dc.relation.referencesFeng, T., et al.: Continuous mobile authentication using touchscreen gestures. In: Proceedings of IEEE International Conference on Biometrics: Theory, Applications and Systems, pp. 451–456 (2013)spa
dc.relation.referencesFrank, M., Biedert, R., Ma, E., Martinovic, I., Song, D.: Touchalytics: on the applicability of touchscreen input as a behavioral biometric for continuous authentication. In: IEEE Transactions on Information Forensics and Security, vol. 8, pp. 136–148 (2013)spa
dc.relation.referencesBhattasali, T., Panasiuk, P., Saeed, K., Chaki, N., Chaki, R.: Modular logic of authentication using dynamic keystroke pattern analysis”. In: Proceedings of ICNAAM, vol. 1738, p. 180012. AIP Publishing, American Institute of Physics (2016)spa
dc.relation.referencesBhattasali, T., Chaki, N., Saeed, K., Chaki, R.: U-stroke pattern modeling for end user identity verification through ubiquitous input device. In: Saeed, K., Homenda, W. (eds.) CISIM 2015. LNCS, vol. 9339, pp. 219–230. Springer, Cham (2015).spa
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