{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T22:12:28Z","timestamp":1780611148517,"version":"3.54.1"},"reference-count":59,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2020,12,17]],"date-time":"2020-12-17T00:00:00Z","timestamp":1608163200000},"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":[[2020,12,17]]},"abstract":"<jats:p>User authentication is key in user authorization on smart and personal devices. Over the years, several authentication mechanisms have been proposed: these also include behavioral-based biometrics. However, behavioral-based biometrics suffer from two issues: they are prone to degradation in performance (accuracy) over time (e.g., due to data distribution changes arising from user behavior) and the need to learn the machine learning model from scratch, when adding new users. In this paper, we propose ContAuth, a system that can enhance the robustness of behavioral-based authentication. ContAuth continuously adapts to new incoming data (data incremental learning) and is able to add new users without retraining (class incremental learning). Specifically, ContAuth combines deep learning models with online learning models to achieve learning on the fly, thereby preventing a severe drop in the accuracy between sessions (over time). To add new users, ContAuth employs class incremental learning methods. We evaluate ContAuth on multiple behavior-based user authentication modalities: breathing, gait. and EMG. Our results show that our framework can help True Positive Rate (TPR) to remain high (&gt;85 %) compared to other methods for all the modalities except EMG (&gt;70%) across the sessions while keeping False Positive Rates (FPR) at a minimum (0-10%). It can achieve up to 35% improvement in TPR over a traditional deep learning model. Additionally, iCaRL (an incremental learning method) enables ContAuth to allow the addition of new users by alleviating catastrophic forgetting, to a large extent. Finally, we also show that ContAuth can be deployed efficiently and effectively on device, further providing data privacy.<\/jats:p>","DOI":"10.1145\/3432203","type":"journal-article","created":{"date-parts":[[2020,12,18]],"date-time":"2020-12-18T15:39:14Z","timestamp":1608305954000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":38,"title":["ContAuth"],"prefix":"10.1145","volume":"4","author":[{"given":"Jagmohan","family":"Chauhan","sequence":"first","affiliation":[{"name":"University of Cambridge"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Young D.","family":"Kwon","sequence":"additional","affiliation":[{"name":"University of Cambridge"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pan","family":"Hui","sequence":"additional","affiliation":[{"name":"University of Helsinki, Hong Kong University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cecilia","family":"Mascolo","sequence":"additional","affiliation":[{"name":"University of Cambridge"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,12,18]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Simone Elsig, Giorgio Giatsidis, Franco Bassetto, and Henning M\u00fcller.","author":"Atzori Manfredo","year":"2014","unstructured":"Manfredo Atzori , Arjan Gijsberts , Claudio Castellini , Barbara Caputo , Anne-Gabrielle Mittaz Hager , Simone Elsig, Giorgio Giatsidis, Franco Bassetto, and Henning M\u00fcller. 2014 . Electromyography data for non-invasive naturally-controlled robotic hand prostheses. Scientific Data 1 (Dec. 2014), 140053. https:\/\/doi.org\/10.1038\/sdata.2014.53 10.1038\/sdata.2014.53 Manfredo Atzori, Arjan Gijsberts, Claudio Castellini, Barbara Caputo, Anne-Gabrielle Mittaz Hager, Simone Elsig, Giorgio Giatsidis, Franco Bassetto, and Henning M\u00fcller. 2014. Electromyography data for non-invasive naturally-controlled robotic hand prostheses. Scientific Data 1 (Dec. 2014), 140053. https:\/\/doi.org\/10.1038\/sdata.2014.53"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of Neuro-N\u0131mes 91","author":"Bottou L\u00e9on","year":"1991","unstructured":"L\u00e9on Bottou . 1991 . Stochastic gradient learning in neural networks . Proceedings of Neuro-N\u0131mes 91 , 8 (1991), 12. L\u00e9on Bottou. 1991. Stochastic gradient learning in neural networks. Proceedings of Neuro-N\u0131mes 91, 8 (1991), 12."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2003.1227990"},{"key":"e_1_2_1_5_1","volume-title":"Torr","author":"Chaudhry Arslan","year":"2018","unstructured":"Arslan Chaudhry , Puneet K. Dokania , Thalaiyasingam Ajanthan , and Philip H. S . Torr . 2018 . Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence . 532--547. http:\/\/openaccess.thecvf.com\/content_ECCV_2018\/html\/Arslan_Chaudhry__Riemannian_Walk_ECCV_2018_paper.html Arslan Chaudhry, Puneet K. Dokania, Thalaiyasingam Ajanthan, and Philip H. S. Torr. 2018. Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence. 532--547. http:\/\/openaccess.thecvf.com\/content_ECCV_2018\/html\/Arslan_Chaudhry__Riemannian_Walk_ECCV_2018_paper.html"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3081333.3081355"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287036"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2018.2381119"},{"key":"e_1_2_1_9_1","volume-title":"BehavioCog: An Observation Resistant Authentication Scheme. In International Conference on Financial Cryptography and Data Security. Springer, 39--58","author":"Chauhan Jagmohan","year":"2017","unstructured":"Jagmohan Chauhan , Benjamin Zi Hao Zhao , Hassan Jameel Asghar , Jonathan Chan , and Mohamed Ali Kaafar . 2017 . BehavioCog: An Observation Resistant Authentication Scheme. In International Conference on Financial Cryptography and Data Security. Springer, 39--58 . Jagmohan Chauhan, Benjamin Zi Hao Zhao, Hassan Jameel Asghar, Jonathan Chan, and Mohamed Ali Kaafar. 2017. BehavioCog: An Observation Resistant Authentication Scheme. In International Conference on Financial Cryptography and Data Security. Springer, 39--58."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/IIHMSP.2010.83"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/PerCom45495.2020.9127387"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2007.383382"},{"key":"e_1_2_1_13_1","volume-title":"Touchalytics: On the applicability of touchscreen input as a behavioral biometric for continuous authentication","author":"Frank Mario","year":"2012","unstructured":"Mario Frank , Ralf Biedert , Eugene Ma , Ivan Martinovic , and Dawn Song . 2012 . Touchalytics: On the applicability of touchscreen input as a behavioral biometric for continuous authentication . IEEE transactions on information forensics and security 8, 1 (2012), 136--148. Mario Frank, Ralf Biedert, Eugene Ma, Ivan Martinovic, and Dawn Song. 2012. Touchalytics: On the applicability of touchscreen input as a behavioral biometric for continuous authentication. IEEE transactions on information forensics and security 8, 1 (2012), 136--148."},{"key":"e_1_2_1_14_1","volume-title":"IDNet: Smartphone-based gait recognition with convolutional neural networks. Pattern Recognition 74 (Feb","author":"Gadaleta Matteo","year":"2018","unstructured":"Matteo Gadaleta and Michele Rossi . 2018. IDNet: Smartphone-based gait recognition with convolutional neural networks. Pattern Recognition 74 (Feb . 2018 ), 25--37. https:\/\/doi.org\/10.1016\/j.patcog.2017.09.005 10.1016\/j.patcog.2017.09.005 Matteo Gadaleta and Michele Rossi. 2018. IDNet: Smartphone-based gait recognition with convolutional neural networks. Pattern Recognition 74 (Feb. 2018), 25--37. https:\/\/doi.org\/10.1016\/j.patcog.2017.09.005"},{"key":"e_1_2_1_15_1","first-page":"51","article-title":"Biometric Gait Authentication Using Accelerometer Sensor","volume":"1","author":"Gafurov Davrondzhon","year":"2006","unstructured":"Davrondzhon Gafurov , Kirsi Helkala , and Torkjel S\u00f8ndrol . 2006 . Biometric Gait Authentication Using Accelerometer Sensor . JCP 1 , 7 (2006), 51 -- 59 . Davrondzhon Gafurov, Kirsi Helkala, and Torkjel S\u00f8ndrol. 2006. Biometric Gait Authentication Using Accelerometer Sensor. JCP 1, 7 (2006), 51--59.","journal-title":"JCP"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2007.902030"},{"key":"e_1_2_1_17_1","volume-title":"Maximilian and Mieszko Lis","author":"Golub Lemieux Guy","year":"2019","unstructured":"Lemieux Guy Golub , Maximilian and Mieszko Lis . 2019 . Full deep neural network training on a pruned weight budget. In SysML. Lemieux Guy Golub, Maximilian and Mieszko Lis. 2019. Full deep neural network training on a pruned weight budget. In SysML."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2015.2437652"},{"key":"e_1_2_1_19_1","volume-title":"Long short-term memory. Neural computation 9, 8","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber . 1997. Long short-term memory. Neural computation 9, 8 ( 1997 ), 1735--1780. Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735--1780."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3214269"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3191744"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOM.2018.8444585"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1093\/oxfordjournals.pan.a004868"},{"key":"e_1_2_1_24_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1611835114"},{"key":"e_1_2_1_26_1","first-page":"57","article-title":"Unobservable re-authentication for smartphones","volume":"56","author":"Li Lingjun","year":"2013","unstructured":"Lingjun Li , Xinxin Zhao , and Guoliang Xue . 2013 . Unobservable re-authentication for smartphones .. In NDSS , Vol. 56. 57 -- 59 . Lingjun Li, Xinxin Zhao, and Guoliang Xue. 2013. Unobservable re-authentication for smartphones.. In NDSS, Vol. 56. 57--59.","journal-title":"NDSS"},{"key":"e_1_2_1_27_1","volume-title":"Learning without forgetting","author":"Li Zhizhong","year":"2017","unstructured":"Zhizhong Li and Derek Hoiem . 2017. Learning without forgetting . IEEE transactions on pattern analysis and machine intelligence 40, 12 ( 2017 ), 2935--2947. Zhizhong Li and Derek Hoiem. 2017. Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40, 12 (2017), 2935--2947."},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155258"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3191751"},{"key":"e_1_2_1_30_1","unstructured":"David Lopez-Paz and Marc'Aurelio Ranzato. 2017. Gradient episodic memory for continual learning. In Advances in Neural Information Processing Systems. 6467--6476.  David Lopez-Paz and Marc'Aurelio Ranzato. 2017. Gradient episodic memory for continual learning. In Advances in Neural Information Processing Systems. 6467--6476."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2004.827237"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2005.1415569"},{"key":"e_1_2_1_33_1","volume-title":"Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychological review 102, 3","author":"McClelland James L","year":"1995","unstructured":"James L McClelland , Bruce L McNaughton , and Randall C O'Reilly . 1995. Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychological review 102, 3 ( 1995 ), 419. James L McClelland, Bruce L McNaughton, and Randall C O'Reilly. 1995. Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychological review 102, 3 (1995), 419."},{"key":"e_1_2_1_34_1","volume-title":"Psychology of learning and motivation.","author":"McCloskey Michael","unstructured":"Michael McCloskey and Neal J Cohen . 1989. Catastrophic interference in connectionist networks: The sequential learning problem . In Psychology of learning and motivation. Vol. 24 . Elsevier , 109--165. Michael McCloskey and Neal J Cohen. 1989. Catastrophic interference in connectionist networks: The sequential learning problem. In Psychology of learning and motivation. Vol. 24. Elsevier, 109--165."},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICORR.2017.8009405"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2009.191"},{"key":"e_1_2_1_37_1","volume-title":"Christopher Kanan, and Stefan Wermter.","author":"Parisi German I","year":"2019","unstructured":"German I Parisi , Ronald Kemker , Jose L Part , Christopher Kanan, and Stefan Wermter. 2019 . Continual lifelong learning with neural networks: A review. Neural Networks ( 2019). German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter. 2019. Continual lifelong learning with neural networks: A review. Neural Networks (2019)."},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3214277"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.587"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2003.1202760"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/2406367.2406384"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3084041.3084061"},{"key":"e_1_2_1_43_1","volume-title":"A study of an EMG-based authentication algorithm using an artificial neural network. In 2017 IEEE SENSORS","author":"Shin Siho","unstructured":"Siho Shin , Jaehyo Jung , and Youn Tae Kim . 2017. A study of an EMG-based authentication algorithm using an artificial neural network. In 2017 IEEE SENSORS . IEEE , 1--3. Siho Shin, Jaehyo Jung, and Youn Tae Kim. 2017. A study of an EMG-based authentication algorithm using an artificial neural network. In 2017 IEEE SENSORS. IEEE, 1--3."},{"key":"e_1_2_1_44_1","volume-title":"2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS). IEEE, 184--188","author":"Shioji Ryohei","year":"2018","unstructured":"Ryohei Shioji , Shin-ichi Ito, Momoyo Ito , and Minoru Fukumi . 2018 . Personal authentication and hand motion recognition based on wrist EMG analysis by a convolutional neural network . In 2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS). IEEE, 184--188 . Ryohei Shioji, Shin-ichi Ito, Momoyo Ito, and Minoru Fukumi. 2018. Personal authentication and hand motion recognition based on wrist EMG analysis by a convolutional neural network. In 2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS). IEEE, 184--188."},{"key":"e_1_2_1_45_1","volume-title":"Woonhoe Goo, Namkeun Kim, Seung-Hoon Chae, and Chang-Geun Ahn.","author":"Sim Joo Yong","year":"2019","unstructured":"Joo Yong Sim , Hyung Wook Noh , Woonhoe Goo, Namkeun Kim, Seung-Hoon Chae, and Chang-Geun Ahn. 2019 . Identity Recognition Based on Bioacoustics of Human Body. IEEE Transactions on Cybernetics ( 2019). Joo Yong Sim, Hyung Wook Noh, Woonhoe Goo, Namkeun Kim, Seung-Hoon Chae, and Chang-Geun Ahn. 2019. Identity Recognition Based on Bioacoustics of Human Body. IEEE Transactions on Cybernetics (2019)."},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2016.7524367"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2012.246"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/1143120.1143128"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3343031.3350904"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3264950"},{"key":"e_1_2_1_51_1","unstructured":"Naigang Wang Jungwook Choi Daniel Brand Chia-Yu Chen and Kailash Gopalakrishnan. 2018. Training deep neural networks with 8-bit floating point numbers. In Advances in neural information processing systems. 7675--7684.  Naigang Wang Jungwook Choi Daniel Brand Chia-Yu Chen and Kailash Gopalakrishnan. 2018. Training deep neural networks with 8-bit floating point numbers. In Advances in neural information processing systems. 7675--7684."},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3054977.3054991"},{"key":"e_1_2_1_53_1","volume-title":"Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics","author":"Yang Jian","year":"2007","unstructured":"Jian Yang , David Zhang , Jing-yu Yang, and Ben Niu . 2007. Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics . IEEE transactions on pattern analysis and machine intelligence 29, 4 ( 2007 ), 650--664. Jian Yang, David Zhang, Jing-yu Yang, and Ben Niu. 2007. Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics. IEEE transactions on pattern analysis and machine intelligence 29, 4 (2007), 650--664."},{"key":"e_1_2_1_54_1","volume-title":"Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network. Frontiers in Neuroscience 11","author":"Zhai Xiaolong","year":"2017","unstructured":"Xiaolong Zhai , Beth Jelfs , Rosa H. M. Chan , and Chung Tin . 2017. Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network. Frontiers in Neuroscience 11 ( 2017 ). https:\/\/doi.org\/10.3389\/fnins.2017.00379 10.3389\/fnins.2017.00379 Xiaolong Zhai, Beth Jelfs, Rosa H. M. Chan, and Chung Tin. 2017. Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network. Frontiers in Neuroscience 11 (2017). https:\/\/doi.org\/10.3389\/fnins.2017.00379"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978296"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6639061"},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICNP.2014.43"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3241539.3241575"},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.2985628"}],"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\/3432203","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3432203","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:47:09Z","timestamp":1750193229000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3432203"}},"subtitle":["Continual Learning Framework for Behavioral-based User Authentication"],"short-title":[],"issued":{"date-parts":[[2020,12,17]]},"references-count":59,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,12,17]]}},"alternative-id":["10.1145\/3432203"],"URL":"https:\/\/doi.org\/10.1145\/3432203","relation":{},"ISSN":["2474-9567"],"issn-type":[{"value":"2474-9567","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,17]]},"assertion":[{"value":"2020-12-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}