Typing pattern analysis for fake profile detection in social Media

Nowadays, 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 rem...

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Autores:
Bhattasali, Tapalina
Saeed, Khalid
Tipo de recurso:
Part of book
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/8831
Acceso en línea:
https://hdl.handle.net/11323/8831
https://repositorio.cuc.edu.co/
Palabra clave:
typing pattern
keystroke
mouse click
touch stroke
fake profile
deep_ID
social media
Rights
closedAccess
License
CC0 1.0 Universal
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oai_identifier_str oai:repositorio.cuc.edu.co:11323/8831
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repository_id_str
dc.title.spa.fl_str_mv Typing pattern analysis for fake profile detection in social Media
title Typing pattern analysis for fake profile detection in social Media
spellingShingle Typing pattern analysis for fake profile detection in social Media
typing pattern
keystroke
mouse click
touch stroke
fake profile
deep_ID
social media
title_short Typing pattern analysis for fake profile detection in social Media
title_full Typing pattern analysis for fake profile detection in social Media
title_fullStr Typing pattern analysis for fake profile detection in social Media
title_full_unstemmed Typing pattern analysis for fake profile detection in social Media
title_sort Typing pattern analysis for fake profile detection in social Media
dc.creator.fl_str_mv Bhattasali, Tapalina
Saeed, Khalid
dc.contributor.author.spa.fl_str_mv Bhattasali, Tapalina
Saeed, Khalid
dc.subject.spa.fl_str_mv typing pattern
keystroke
mouse click
touch stroke
fake profile
deep_ID
social media
topic typing pattern
keystroke
mouse click
touch stroke
fake profile
deep_ID
social media
description Nowadays, 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.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-11-03T14:23:36Z
dc.date.available.none.fl_str_mv 2021-11-03T14:23:36Z
dc.date.issued.none.fl_str_mv 2021-09-17
dc.type.spa.fl_str_mv Capítulo - Parte de Libro
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dc.identifier.doi.spa.fl_str_mv 10.1007/978-3-030-84340-3_2
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
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identifier_str_mv 03029743
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Corporación Universidad de la Costa
REDICUC - Repositorio CUC
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dc.language.iso.none.fl_str_mv eng
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dc.relation.references.spa.fl_str_mv Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
Cruz, 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)
Sun, 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_19
Zhong, 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)
Baynath, 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)
Liu, 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)
Lever, J., Krzywinski, M., Altman, N.: Points of Significance: principal component analysis. Nat. Methods 14(7), 641–642 (2017)
Sherstinsky, A.: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D 404, 132306 (2020)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Du, 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)
Obaidat, M.S., Sadoun, B.: Verification of computer users using keystroke dynamics. IEEE Trans. Syst. Man Cybern. B Cybern. 27(2), 261–269 (1997)
Zhao, 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)
Abramson, M., Gore, S.: Associative patterns of web browsing behavior. In: AAAIFall Symposium Series (2013)
Zhang, 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_24
Ahmed, A.A., Traore, I.: Biometric recognition based on free-text keystroke dynamics. IEEE Trans, Cybern. 44(4), 458–472 (2014)
Killourhy, 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)
Killourhy, 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)
Bhattasali, 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)
Bhattasali, T., Saeed, K., Chaki, N., Chaki, R.: Bio-authentication for layered remote health monitor framework. J. Med. Inform. Technol. 23, 131–140 (2014)
Maxion, R., Killourhy, K.: Keystroke biometrics with number-pad input. In: Proceedings of IEEE International Conference on Dependable Systems & Networks, pp. 201–210 (2010)
Xu, 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)
Feng, 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)
Frank, 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)
Bhattasali, 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)
Bhattasali, 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).
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spelling Bhattasali, TapalinaSaeed, Khalid2021-11-03T14:23:36Z2021-11-03T14:23:36Z2021-09-1703029743https://hdl.handle.net/11323/883110.1007/978-3-030-84340-3_2Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Nowadays, 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.Bhattasali, Tapalina-will be generated-orcid-0000-0001-7799-2720-600Saeed, Khalid-will be generated-orcid-0000-0002-7741-7045-600application/pdfengComputer Information Systems and Industrial ManagementCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbInternational Conference on Computer Information Systems and Industrial Management CISIM 2021https://link.springer.com/chapter/10.1007/978-3-030-84340-3_2typing patternkeystrokemouse clicktouch strokefake profiledeep_IDsocial mediaTyping pattern analysis for fake profile detection in social MediaCapítulo - Parte de Librohttp://purl.org/coar/resource_type/c_3248Textinfo:eu-repo/semantics/bookParthttp://purl.org/redcol/resource_type/CAP_LIBinfo:eu-repo/semantics/acceptedVersionHinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)Cruz, 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)Sun, 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_19Zhong, 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)Baynath, 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)Liu, 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)Lever, J., Krzywinski, M., Altman, N.: Points of Significance: principal component analysis. Nat. Methods 14(7), 641–642 (2017)Sherstinsky, A.: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D 404, 132306 (2020)Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)Du, 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)Obaidat, M.S., Sadoun, B.: Verification of computer users using keystroke dynamics. IEEE Trans. Syst. Man Cybern. B Cybern. 27(2), 261–269 (1997)Zhao, 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)Abramson, M., Gore, S.: Associative patterns of web browsing behavior. In: AAAIFall Symposium Series (2013)Zhang, 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_24Ahmed, A.A., Traore, I.: Biometric recognition based on free-text keystroke dynamics. IEEE Trans, Cybern. 44(4), 458–472 (2014)Killourhy, 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)Killourhy, 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)Bhattasali, 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)Bhattasali, T., Saeed, K., Chaki, N., Chaki, R.: Bio-authentication for layered remote health monitor framework. J. Med. Inform. Technol. 23, 131–140 (2014)Maxion, R., Killourhy, K.: Keystroke biometrics with number-pad input. In: Proceedings of IEEE International Conference on Dependable Systems & Networks, pp. 201–210 (2010)Xu, 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)Feng, 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)Frank, 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)Bhattasali, 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)Bhattasali, 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. 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