{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T04:03:03Z","timestamp":1780113783521,"version":"3.54.0"},"reference-count":50,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2023,6,12]],"date-time":"2023-06-12T00:00:00Z","timestamp":1686528000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2023,6,12]]},"abstract":"<jats:p>One of the main challenges in deploying a keystroke dynamics-based continuous authentication scheme on smartphones is ensuring low error rates over time. Unstable false rejection rates (FRRs) would lead to frequent phone locks during long-term use, and deteriorating attack detection rates would jeopardize its security benefits. The fact that it is undesirable to train complex deep learning models directly on smartphones or send private sensor data to servers for training present unique deployment constraints, requiring on-device solutions that can be trained fully on smartphones.<\/jats:p>\n          <jats:p>To improve authentication accuracy while satisfying such real-world deployment constraints, we propose two novel feature engineering techniques: (1) computation of pair-wise correlations between accelerometer and gyroscope sensor values, and (2) on-device feature extraction technique to compute dynamic time warping (DTW) distance measurements between autoencoder inputs and outputs via transfer-learning. Using those two feature sets in an ensemble blender, we achieved 6.4 percent equal error rate (EER) in a public dataset. In comparison, blending two state-of-the-art solutions achieved 14.1 percent EER in the same test settings. Our real-world dataset evaluation showed increasing FRRs (user frustration) over two months; however, through periodic model retraining, we were able to maintain average FRRs around 2.5 percent while keeping attack detection rates around 89 percent. The proposed solution has been deployed in the latest Samsung Galaxy smartphone series to protect secure workspace through continuous authentication.<\/jats:p>","DOI":"10.1145\/3596236","type":"journal-article","created":{"date-parts":[[2023,6,12]],"date-time":"2023-06-12T18:58:16Z","timestamp":1686596296000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["On the Long-Term Effects of Continuous Keystroke Authentication"],"prefix":"10.1145","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2007-4018","authenticated-orcid":false,"given":"Jun Ho","family":"Huh","sequence":"first","affiliation":[{"name":"Samsung Research, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6912-6452","authenticated-orcid":false,"given":"Sungsu","family":"Kwag","sequence":"additional","affiliation":[{"name":"Samsung Research, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0074-6805","authenticated-orcid":false,"given":"Iljoo","family":"Kim","sequence":"additional","affiliation":[{"name":"Samsung Research, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1377-904X","authenticated-orcid":false,"given":"Alexandr","family":"Popov","sequence":"additional","affiliation":[{"name":"Samsung R&amp;D Institute Ukraine, Kiev, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6127-5427","authenticated-orcid":false,"given":"Younghan","family":"Park","sequence":"additional","affiliation":[{"name":"Moloco Inc., Redwood City, CA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3679-416X","authenticated-orcid":false,"given":"Geumhwan","family":"Cho","sequence":"additional","affiliation":[{"name":"Sungkyunkwan University, Suwon, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9726-4837","authenticated-orcid":false,"given":"Juwon","family":"Lee","sequence":"additional","affiliation":[{"name":"Samsung Research, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1605-3866","authenticated-orcid":false,"given":"Hyoungshick","family":"Kim","sequence":"additional","affiliation":[{"name":"Sungkyunkwan University, Suwon, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5146-0259","authenticated-orcid":false,"given":"Choong-Hoon","family":"Lee","sequence":"additional","affiliation":[{"name":"Samsung Research, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,6,12]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2975779"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3272034"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377404"},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the 30th International Conference on International Conference on Machine Learning (ICML).","author":"Bergstra J.","unstructured":"J. Bergstra, D. Yamins, and D. D. Cox. 2013. Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures. In Proceedings of the 30th International Conference on International Conference on Machine Learning (ICML)."},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining (AAAIWS).","author":"Donald","unstructured":"Donald J. Berndt and James Clifford. 1994. Using Dynamic Time Warping to Find Patterns in Time Series. In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining (AAAIWS)."},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of the 16th International Conference on Financial Cryptography and Data Security (FC).","author":"Bonneau Joseph","unstructured":"Joseph Bonneau, S\u00f6ren Preibusch, and Ross J. Anderson. 2012. A Birthday Present Every Eleven Wallets? The Security of Customer-Chosen Banking PINs. In Proceedings of the 16th International Conference on Financial Cryptography and Data Security (FC)."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3173574.3173829"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2702123.2702252"},{"key":"e_1_2_1_9_1","volume-title":"Proceedings of the 15th Annual Conference on Privacy, Security and Trust (PST).","author":"Centeno M. P.","unstructured":"M. P. Centeno, A. v. Moorsel, and S. Castruccio. 2017. Smartphone Continuous Authentication Using Deep Learning Autoencoders. In Proceedings of the 15th Annual Conference on Privacy, Security and Trust (PST)."},{"key":"e_1_2_1_10_1","volume-title":"Proceedings of the 12th ACM on Asia Conference on Computer and Communications Security (ASIACCS).","author":"Cha S.","unstructured":"S. Cha, S. Kwag, H. Kim, and J. H. Huh. 2017. Boosting the Guessing Attack Performance on Android Lock Patterns with Smudge Attacks. In Proceedings of the 12th ACM on Asia Conference on Computer and Communications Security (ASIACCS)."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3432203"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/WTS.2018.8363930"},{"key":"e_1_2_1_13_1","volume-title":"Proceedings of the 38th IEEE Symposium on Security and Privacy (S&P).","author":"Cho G.","unstructured":"G. Cho, J. H. Huh, J. Cho, S. Oh, Y. Song, and H. Kim. 2017. SysPal: System-Guided Pattern Locks for Android. In Proceedings of the 38th IEEE Symposium on Security and Privacy (S&P)."},{"key":"e_1_2_1_14_1","unstructured":"Francois Chollet et al. 2015. Keras. https:\/\/github.com\/fchollet\/keras"},{"key":"e_1_2_1_15_1","volume-title":"Proceedings of the 13th Symposium on Usable Privacy and Security (SOUPS).","author":"Crawford Heather","year":"2017","unstructured":"Heather Crawford and Ebad Ahmadzadeh. 2017. Authentication on the Go: Assessing the Effect of Movement on Mobile Device Keystroke Dynamics. In Proceedings of the 13th Symposium on Usable Privacy and Security (SOUPS)."},{"key":"e_1_2_1_16_1","volume-title":"Proceeding of 12th IAPR International Conference on Biometrics (ICB).","author":"Deb Debayan","unstructured":"Debayan Deb, Arun Ross, Anil K. Jain, Kwaku Prakah-Asante, and K. Venkatesh Prasad. 2019. Actions Speak Louder Than (Pass)words: Passive Authentication of Smartphone Users via Deep Temporal Features. In Proceeding of 12th IAPR International Conference on Biometrics (ICB)."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2660267.2660273"},{"key":"e_1_2_1_18_1","volume-title":"Retrieved","author":"Authority European Banking","year":"2019","unstructured":"European Banking Authority. 2019. EBA publishes an Opinion on the elements of strong customer authentication under PSD2. Retrieved June 21, 2019 from https:\/\/www.eba.europa.eu\/eba-publishes-an-opinion-on-the-elements-of-strong-customer-authentication-under-psd2"},{"key":"e_1_2_1_19_1","volume-title":"Deep learning for time series classification: a review. Data Mining and Knowledge Discovery 33","author":"Fawaz Hassan Ismail","year":"2019","unstructured":"Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, and Pierre-Alain Muller. 2019. Deep learning for time series classification: a review. Data Mining and Knowledge Discovery 33 (2019)."},{"key":"e_1_2_1_20_1","volume-title":"Statistical methods for rates and proportions","author":"Fleiss Joseph L","unstructured":"Joseph L Fleiss, Bruce Levin, and Myunghee Cho Paik. 2013. Statistical methods for rates and proportions. John Wiley & Sons."},{"key":"e_1_2_1_21_1","volume-title":"Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package. Journal of Statistical Software 31","author":"Giorgino T.","year":"2009","unstructured":"T. Giorgino. 2009. Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package. Journal of Statistical Software 31 (2009)."},{"key":"e_1_2_1_22_1","volume-title":"Deep Learning","author":"Goodfellow Ian J.","unstructured":"Ian J. Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT Press."},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2858036.2858267"},{"key":"e_1_2_1_24_1","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS).","author":"Ke Guolin","year":"2017","unstructured":"Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS)."},{"key":"e_1_2_1_25_1","volume-title":"Sometimes Annoying. In Proceedings of the 11th Symposium On Usable Privacy and Security (SOUPS).","author":"Khan Hassan","year":"2015","unstructured":"Hassan Khan, Urs Hengartner, and Daniel Vogel. 2015. Usability and Security Perceptions of Implicit Authentication: Convenient, Secure, Sometimes Annoying. In Proceedings of the 11th Symposium On Usable Privacy and Security (SOUPS)."},{"key":"e_1_2_1_26_1","volume-title":"Accessibility, Universality","author":"MacKenzie I. Scott","unstructured":"I. Scott MacKenzie and Kumiko Tanaka-Ishii. 2007. Text Entry Systems: Mobility, Accessibility, Universality. Morgan Kaufmann Publishers Inc."},{"key":"e_1_2_1_27_1","unstructured":"Kathleen Macqueen Eleanor McLellan-Lemal K. Bartholow and B. Milstein. 2008. Team-based codebook development: Structure process and agreement. Handbook for team-based qualitative research (2008)."},{"key":"e_1_2_1_28_1","volume-title":"Proceedings of the 41st IEEE Symposium on Security and Privacy (S&P).","author":"Markert Philipp","unstructured":"Philipp Markert, Daniel V. Bailey, Maximilian Golla, Markus D\u00fcrmuth, and Adam J. Aviv. 2020. This PIN Can Be Easily Guessed: Analyzing the Security of Smartphone Unlock PINs. In Proceedings of the 41st IEEE Symposium on Security and Privacy (S&P)."},{"key":"e_1_2_1_29_1","volume-title":"Kitsune: an ensemble of autoencoders for online network intrusion detection. arXiv preprint arXiv:1802.09089","author":"Mirsky Yisroel","year":"2018","unstructured":"Yisroel Mirsky, Tomer Doitshman, Yuval Elovici, and Asaf Shabtai. 2018. Kitsune: an ensemble of autoencoders for online network intrusion detection. arXiv preprint arXiv:1802.09089 (2018)."},{"key":"e_1_2_1_30_1","volume-title":"Proceedings of the 17th IEEE International Symposium on High Assurance Systems Engineering (HASE).","author":"Palaskar N.","unstructured":"N. Palaskar, Z. Syed, S. Banerjee, and C. Tang. 2016. Empirical Techniques to Detect and Mitigate the Effects of Irrevocably Evolving User Profiles in Touch-Based Authentication Systems. In Proceedings of the 17th IEEE International Symposium on High Assurance Systems Engineering (HASE)."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2016.2555335"},{"key":"e_1_2_1_33_1","volume-title":"Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12","author":"Pedregosa F.","year":"2011","unstructured":"F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011)."},{"key":"e_1_2_1_34_1","unstructured":"Android Developers Portal. n.d.. Android dumpsys tool. https:\/\/developer.android.com\/studio\/command-line\/dumpsys?hl=en"},{"key":"e_1_2_1_35_1","volume-title":"Retrieved","author":"Portal Google Developers","year":"2020","unstructured":"Google Developers Portal. 2020. Activity Recognition Transition API Codelab. Retrieved September 16, 2020 from https:\/\/codelabs.developers.google.com\/codelabs\/activity-recognition-transition\/index.html#6"},{"key":"e_1_2_1_36_1","volume-title":"Shaker El-Sappagh, and Jong-Wan Hu.","author":"Saini Baljit Singh","year":"2020","unstructured":"Baljit Singh Saini, Parminder Singh, Anand Nayyar, Navdeep Kaur, Kamaljit Singh Bhatia, Shaker El-Sappagh, and Jong-Wan Hu. 2020. A Three-Step Authentication Model for Mobile Phone User Using Keystroke Dynamics. IEEE Access 8, 3 (2020)."},{"key":"e_1_2_1_37_1","volume-title":"Russian Sentence Corpus: Benchmark measures of eye movements in reading in Russian. Behavior Research Methods 51","author":"Sekerina Irina","year":"2019","unstructured":"Irina Sekerina, Anna Laurinavichyute, Svetlana Alexeeva, Kristina Bagdasaryan, and Reinhold Kliegl. 2019. Russian Sentence Corpus: Benchmark measures of eye movements in reading in Russian. Behavior Research Methods 51 (2019)."},{"key":"e_1_2_1_38_1","doi-asserted-by":"crossref","unstructured":"Chao Shen Y. Li Yufei Chen X. Guan and R. Maxion. 2018. Performance Analysis of Multi-Motion Sensor Behavior for Active Smartphone Authentication. IEEE Transactions on Information Forensics and Security 13 (2018).","DOI":"10.1109\/TIFS.2017.2737969"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448080"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/WiMOB.2011.6085412"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2015.2506542"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.14722\/madweb.2020.23011"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2019.23351"},{"key":"e_1_2_1_44_1","unstructured":"Caroline Tagg. 2009. A corpus linguistics study of SMS text messaging."},{"key":"e_1_2_1_45_1","volume-title":"Mobile Technology and Home Broadband","author":"PEW Research Center","year":"2019","unstructured":"PEW Research Center. 2019. Mobile Technology and Home Broadband 2019. https:\/\/www.pewresearch.org\/internet\/2019\/06\/13\/mobile-technology-and-home-broadband-2019\/"},{"key":"e_1_2_1_46_1","unstructured":"Stefan Trost Media. n.d.. Character Frequencies. https:\/\/www.sttmedia.com\/characterfrequencies"},{"key":"e_1_2_1_47_1","volume-title":"Recent Advances in Autoencoder-Based Representation Learning. CoRR abs\/1812.05069","author":"Tschannen Michael","year":"2018","unstructured":"Michael Tschannen, Olivier Bachem, and Mario Lucic. 2018. Recent Advances in Autoencoder-Based Representation Learning. CoRR abs\/1812.05069 (2018). arXiv:1812.05069 http:\/\/arxiv.org\/abs\/1812.05069"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/2508859.2516700"},{"key":"e_1_2_1_49_1","volume-title":"Proceedings of the 10th Symposium On Usable Privacy and Security (SOUPS).","author":"Xu Hui","unstructured":"Hui Xu, Yangfan Zhou, and Michael R. Lyu. 2014. Towards Continuous and Passive Authentication via Touch Biometrics: An Experimental Study on Smartphones. In Proceedings of the 10th Symposium On Usable Privacy and Security (SOUPS)."},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3432192"}],"container-title":["Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3596236","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3596236","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T04:46:51Z","timestamp":1752468411000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3596236"}},"subtitle":["Keeping User Frustration Low through Behavior Adaptation"],"short-title":[],"issued":{"date-parts":[[2023,6,12]]},"references-count":50,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,6,12]]}},"alternative-id":["10.1145\/3596236"],"URL":"https:\/\/doi.org\/10.1145\/3596236","relation":{},"ISSN":["2474-9567"],"issn-type":[{"value":"2474-9567","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,12]]},"assertion":[{"value":"2023-06-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}