{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T16:50:59Z","timestamp":1770223859689,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":77,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T00:00:00Z","timestamp":1649894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSF Division of Computer and Network Systems (CNS)","award":["1553696"],"award-info":[{"award-number":["1553696"]}]},{"name":"National Science Foundation (NSF)","award":["1736209"],"award-info":[{"award-number":["1736209"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,4,14]]},"DOI":"10.1145\/3508398.3511497","type":"proceedings-article","created":{"date-parts":[[2022,4,16]],"date-time":"2022-04-16T04:13:31Z","timestamp":1650082411000},"page":"143-154","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":49,"title":["Toward Deep Learning Based Access Control"],"prefix":"10.1145","author":[{"given":"Mohammad Nur","family":"Nobi","sequence":"first","affiliation":[{"name":"University of Texas at San Antonio, San Antonio, TX, USA"}]},{"given":"Ram","family":"Krishnan","sequence":"additional","affiliation":[{"name":"University of Texas at San Antonio, San Antonio, TX, USA"}]},{"given":"Yufei","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Pittsburgh &amp; UPMC Hillman Cancer Center, Pittsburgh, PA, USA"}]},{"given":"Mehrnoosh","family":"Shakarami","sequence":"additional","affiliation":[{"name":"University of Texas at San Antonio, San Antonio, TX, USA"}]},{"given":"Ravi","family":"Sandhu","sequence":"additional","affiliation":[{"name":"University of Texas at San Antonio, San Antonio, TX, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,4,15]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"crossref","unstructured":"Manar Alohaly Hassan Takabi and Eduardo Blanco. 2018. A deep learning approach for extracting attributes of ABAC policies. In SACMAT. ACM.  Manar Alohaly Hassan Takabi and Eduardo Blanco. 2018. A deep learning approach for extracting attributes of ABAC policies. In SACMAT. ACM.","DOI":"10.1145\/3205977.3205984"},{"key":"e_1_3_2_2_2_1","unstructured":"Kaggle Amazon. 2013. Amazon Employee Access Challenge in Kaggle. https:\/\/www.kaggle.com\/c\/amazon-employee-access-challenge\/  Kaggle Amazon. 2013. Amazon Employee Access Challenge in Kaggle. https:\/\/www.kaggle.com\/c\/amazon-employee-access-challenge\/"},{"key":"e_1_3_2_2_3_1","unstructured":"UCI Amazon. 2011. Amazon Access Samples Data Set. http:\/\/archive.ics.uci.edu\/ml\/datasets\/Amazon  UCI Amazon. 2011. Amazon Access Samples Data Set. http:\/\/archive.ics.uci.edu\/ml\/datasets\/Amazon"},{"key":"e_1_3_2_2_4_1","unstructured":"Access  Access"},{"key":"e_1_3_2_2_5_1","unstructured":"Samples  Samples"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1518701.1518838"},{"key":"e_1_3_2_2_7_1","volume-title":"Random forests. Machine learning","author":"Breiman Leo","year":"2001","unstructured":"Leo Breiman . 2001. Random forests. Machine learning ( 2001 ). Leo Breiman. 2001. Random forests. Machine learning (2001)."},{"key":"e_1_3_2_2_8_1","volume-title":"mbox","author":"T. Bui","year":"2017","unstructured":"T. Bui et al mbox . 2017 . Mining relationship-based access control policies. In SACMAT. T. Bui et almbox. 2017. Mining relationship-based access control policies. In SACMAT."},{"key":"e_1_3_2_2_9_1","volume-title":"Symposium on Foundations and Practice of Security.","author":"T. Bui","year":"2018","unstructured":"T. Bui et almbox. 2018 . Mining Relationship-Based Access Control Policies from Incomplete and Noisy Data . In Symposium on Foundations and Practice of Security. T. Bui et almbox. 2018. Mining Relationship-Based Access Control Policies from Incomplete and Noisy Data. In Symposium on Foundations and Practice of Security."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"crossref","unstructured":"Thang Bui and Scott D Stoller. 2020 a. A decision tree learning approach for mining relationship-based access control policies. In SACMAT. ACM.  Thang Bui and Scott D Stoller. 2020 a. A decision tree learning approach for mining relationship-based access control policies. In SACMAT. ACM.","DOI":"10.1145\/3381991.3395619"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"crossref","unstructured":"T. Bui and S. D Stoller. 2020 b. Learning Attribute-Based and Relationship-Based Access Control Policies with Unknown Values. In Information Systems Security.  T. Bui and S. D Stoller. 2020 b. Learning Attribute-Based and Relationship-Based Access Control Policies with Unknown Values. In Information Systems Security.","DOI":"10.1007\/978-3-030-65610-2_2"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"crossref","unstructured":"Thang Bui Scott D Stoller and Hieu Le. 2019 a. Efficient and Extensible Policy Mining for Relationship-Based Access Control. In SACMAT. ACM.  Thang Bui Scott D Stoller and Hieu Le. 2019 a. Efficient and Extensible Policy Mining for Relationship-Based Access Control. In SACMAT. ACM.","DOI":"10.1145\/3322431.3325106"},{"key":"e_1_3_2_2_13_1","volume-title":"2019 b. Greedy and evolutionary algorithms for mining relationship-based access control policies. Computers & Security","author":"Bui T.","year":"2019","unstructured":"T. Bui , S. D Stoller , and J. Li . 2019 b. Greedy and evolutionary algorithms for mining relationship-based access control policies. Computers & Security ( 2019 ). T. Bui, S. D Stoller, and J. Li. 2019 b. Greedy and evolutionary algorithms for mining relationship-based access control policies. Computers & Security (2019)."},{"key":"e_1_3_2_2_14_1","volume-title":"mbox","author":"Luca Cappelletti","year":"2019","unstructured":"Luca Cappelletti et al mbox . 2019 . On the quality of classification models for inferring abac policies from access logs. In Big Data. IEEE. Luca Cappelletti et almbox. 2019. On the quality of classification models for inferring abac policies from access logs. In Big Data. IEEE."},{"key":"e_1_3_2_2_15_1","first-page":"264","article-title":"Generation of attribute based access control policy from existing authorization system","volume":"9","author":"Chari Suresh N","year":"2016","unstructured":"Suresh N Chari and Ian M Molloy . 2016 . Generation of attribute based access control policy from existing authorization system . US Patent 9 , 264 ,451. Suresh N Chari and Ian M Molloy. 2016. Generation of attribute based access control policy from existing authorization system. US Patent 9,264,451.","journal-title":"US Patent"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"crossref","unstructured":"Y. Cheng K. Bijon and R. Sandhu. 2016. Extended ReBAC administrative models with cascading revocation and provenance support. In SACMAT. ACM.  Y. Cheng K. Bijon and R. Sandhu. 2016. Extended ReBAC administrative models with cascading revocation and provenance support. In SACMAT. ACM.","DOI":"10.1145\/2914642.2914655"},{"key":"e_1_3_2_2_17_1","volume-title":"Xception: Deep learning with depthwise separable convolutions","author":"Chollet Francc","year":"2017","unstructured":"Francc ois Chollet . 2017 . Xception: Deep learning with depthwise separable convolutions . In IEEE CVPR. Francc ois Chollet. 2017. Xception: Deep learning with depthwise separable convolutions. In IEEE CVPR."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"crossref","unstructured":"C. Cortes and V. Vapnik. 1995. Support-vector networks. Machine learning (1995).  C. Cortes and V. Vapnik. 1995. Support-vector networks. Machine learning (1995).","DOI":"10.1007\/BF00994018"},{"key":"e_1_3_2_2_19_1","volume-title":"mbox","author":"C. Cotrini","year":"2018","unstructured":"C. Cotrini et al mbox . 2018 . Mining ABAC rules from sparse logs. In Euro S &P. C. Cotrini et almbox. 2018. Mining ABAC rules from sparse logs. In Euro S&P."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"crossref","unstructured":"Carlos Cotrini Luca Corinzia Thilo Weghorn and David Basin. 2019. The next 700 policy miners: A universal method for building policy miners. In CCS. ACM.  Carlos Cotrini Luca Corinzia Thilo Weghorn and David Basin. 2019. The next 700 policy miners: A universal method for building policy miners. In CCS. ACM.","DOI":"10.1145\/3319535.3354196"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"crossref","unstructured":"S. Das B. Mitra V. Atluri J. Vaidya and S. Sural. 2018. Policy Engineering in RBAC and ABAC. In From Database to Cyber Security. Springer.  S. Das B. Mitra V. Atluri J. Vaidya and S. Sural. 2018. Policy Engineering in RBAC and ABAC. In From Database to Cyber Security. Springer.","DOI":"10.1007\/978-3-030-04834-1_2"},{"key":"e_1_3_2_2_22_1","volume-title":"Intl. Conference on Networked Systems.","author":"M.","unstructured":"M. A El Hadj, Y. Benkaouz, B. Freisleben, and M. Erradi. 2017. ABAC rule reduction via similarity computation . In Intl. Conference on Networked Systems. M. A El Hadj, Y. Benkaouz, B. Freisleben, and M. Erradi. 2017. ABAC rule reduction via similarity computation. In Intl. Conference on Networked Systems."},{"key":"e_1_3_2_2_23_1","unstructured":"David F Ferraiolo Dennis M Gilbert and Nickilyn Lynch. 1995. An examination of federal and commercial access control policy needs. In NIST-NCSC.  David F Ferraiolo Dennis M Gilbert and Nickilyn Lynch. 1995. An examination of federal and commercial access control policy needs. In NIST-NCSC."},{"key":"e_1_3_2_2_24_1","volume-title":"mbox","author":"M. Frank","year":"2008","unstructured":"M. Frank et al mbox . 2008 . A class of probabilistic models for role engineering. In CCS. M. Frank et almbox. 2008. A class of probabilistic models for role engineering. In CCS."},{"key":"e_1_3_2_2_25_1","volume-title":"mbox","author":"M. Frank","year":"2009","unstructured":"M. Frank et al mbox . 2009 . A probabilistic approach to hybrid role mining. In CCS. M. Frank et almbox. 2009. A probabilistic approach to hybrid role mining. In CCS."},{"key":"e_1_3_2_2_26_1","volume-title":"mbox","author":"M. Frank","year":"2013","unstructured":"M. Frank et al mbox . 2013 . Role mining with probabilistic models. TISSEC ( 2013). M. Frank et almbox. 2013. Role mining with probabilistic models. TISSEC (2013)."},{"key":"e_1_3_2_2_27_1","volume-title":"mbox","author":"S. Fukui","year":"2019","unstructured":"S. Fukui et al mbox . 2019 . Distilling Knowledge for Non-Neural Networks. In APSIPA. S. Fukui et almbox. 2019. Distilling Knowledge for Non-Neural Networks. In APSIPA."},{"key":"e_1_3_2_2_28_1","volume-title":"Semantics for global and local interpretation of deep neural networks. arXiv:1910.09085","author":"Gu Jindong","year":"2019","unstructured":"Jindong Gu and Volker Tresp . 2019. Semantics for global and local interpretation of deep neural networks. arXiv:1910.09085 ( 2019 ). Jindong Gu and Volker Tresp. 2019. Semantics for global and local interpretation of deep neural networks. arXiv:1910.09085 (2019)."},{"key":"e_1_3_2_2_29_1","volume-title":"mbox","author":"J. Hancock","year":"2020","unstructured":"J. Hancock et al mbox . 2020 . Survey on categorical data for neural networks. Big Data . J. Hancock et almbox. 2020. Survey on categorical data for neural networks. Big Data."},{"key":"e_1_3_2_2_30_1","volume-title":"A Harrison et almbox","author":"M.","year":"1976","unstructured":"M. A Harrison et almbox . 1976 . Protection in operating systems. Commun. ACM. M. A Harrison et almbox. 1976. Protection in operating systems. Commun. ACM."},{"key":"e_1_3_2_2_31_1","volume-title":"mbox","author":"Kaiming He","year":"2016","unstructured":"Kaiming He et al mbox . 2016 . Deep residual learning for image recognition. In CVPR. Kaiming He et almbox. 2016. Deep residual learning for image recognition. In CVPR."},{"key":"e_1_3_2_2_32_1","volume-title":"mbox","author":"G. Hinton","year":"2015","unstructured":"G. Hinton et al mbox . 2015 . Distilling the knowledge in a neural network. arXiv. G. Hinton et almbox. 2015. Distilling the knowledge in a neural network. arXiv."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2962617"},{"key":"e_1_3_2_2_34_1","volume-title":"C Hu et almbox","author":"V.","year":"2013","unstructured":"V. C Hu et almbox . 2013 . Guide to attribute based access control (ABAC) definition and considerations (draft). NIST special publication (2013). V. C Hu et almbox. 2013. Guide to attribute based access control (ABAC) definition and considerations (draft). NIST special publication (2013)."},{"key":"e_1_3_2_2_35_1","volume-title":"mbox","author":"Gao Huang","year":"2017","unstructured":"Gao Huang et al mbox . 2017 . Densely connected convolutional networks. In CVPR. Gao Huang et almbox. 2017. Densely connected convolutional networks. In CVPR."},{"key":"e_1_3_2_2_36_1","unstructured":"Padmavathi Iyer and Amirreza Masoumzadeh. 2018. Mining positive and negative attribute-based access control policy rules. In SACMAT. ACM.  Padmavathi Iyer and Amirreza Masoumzadeh. 2018. Mining positive and negative attribute-based access control policy rules. In SACMAT. ACM."},{"key":"e_1_3_2_2_37_1","unstructured":"Padmavathi Iyer and Amirreza Masoumzadeh. 2019. Generalized Mining of Relationship-Based Access Control Policies in Evolving Systems. In SACMAT.  Padmavathi Iyer and Amirreza Masoumzadeh. 2019. Generalized Mining of Relationship-Based Access Control Policies in Evolving Systems. In SACMAT."},{"key":"e_1_3_2_2_38_1","unstructured":"Padmavathi Iyer and Amirreza Masoumzadeh. 2020. Active learning of relationship-based access control policies. In SACMAT.  Padmavathi Iyer and Amirreza Masoumzadeh. 2020. Active learning of relationship-based access control policies. In SACMAT."},{"key":"e_1_3_2_2_39_1","volume-title":"et almbox","author":"Jabal Amani Abu","year":"2020","unstructured":"Amani Abu Jabal , Elisa Bertino , et almbox . 2020 . Polisma-a framework for learning attribute-based access control policies. In ESORICS. Amani Abu Jabal, Elisa Bertino, et almbox. 2020. Polisma-a framework for learning attribute-based access control policies. In ESORICS."},{"key":"e_1_3_2_2_40_1","volume-title":"et almbox","author":"Jafarian J. H","year":"2015","unstructured":"J. H Jafarian , H. Takabi , et almbox . 2015 . Towards a general framework for optimal role mining: A constraint satisfaction approach. In SACMAT. J. H Jafarian, H. Takabi, et almbox. 2015. Towards a general framework for optimal role mining: A constraint satisfaction approach. In SACMAT."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"crossref","unstructured":"L. Karimi M. Aldairi J. Joshi and M. Abdelhakim. 2021. An automatic attribute based access control policy extraction from access logs. IEEE TDSC (2021).  L. Karimi M. Aldairi J. Joshi and M. Abdelhakim. 2021. An automatic attribute based access control policy extraction from access logs. IEEE TDSC (2021).","DOI":"10.1109\/TDSC.2021.3054331"},{"key":"e_1_3_2_2_42_1","volume-title":"Big Data","author":"Karimi Leila","unstructured":"Leila Karimi and James Joshi . 2018. An unsupervised learning based approach for mining attribute based access control policies . In Big Data . IEEE. Leila Karimi and James Joshi. 2018. An unsupervised learning based approach for mining attribute based access control policies. In Big Data. IEEE."},{"key":"e_1_3_2_2_43_1","volume-title":"From ABAC to ZBAC: the evolution of access control models. Journal of Information Warfare","author":"Karp Alan H","year":"2010","unstructured":"Alan H Karp , Harry Haury , and Michael H Davis . 2010. From ABAC to ZBAC: the evolution of access control models. Journal of Information Warfare ( 2010 ). Alan H Karp, Harry Haury, and Michael H Davis. 2010. From ABAC to ZBAC: the evolution of access control models. Journal of Information Warfare (2010)."},{"key":"e_1_3_2_2_44_1","volume-title":"APRIORI-SD: Adapting association rule learning to subgroup discovery. Applied Artificial Intelligence","author":"Nada Lavravc Branko Kavvs","year":"2006","unstructured":"Branko Kavvs ek and Nada Lavravc . 2006. APRIORI-SD: Adapting association rule learning to subgroup discovery. Applied Artificial Intelligence ( 2006 ). Branko Kavvs ek and Nada Lavravc. 2006. APRIORI-SD: Adapting association rule learning to subgroup discovery. Applied Artificial Intelligence (2006)."},{"key":"e_1_3_2_2_45_1","volume-title":"mbox","author":"A. Kaya","year":"2019","unstructured":"A. Kaya et al mbox . 2019 . Analysis of transfer learning for deep neural network based plant classification models. Computers and electronics in agriculture (2019). A. Kaya et almbox. 2019. Analysis of transfer learning for deep neural network based plant classification models. Computers and electronics in agriculture (2019)."},{"key":"e_1_3_2_2_46_1","volume-title":"Intl. Workshop on Risk Assessment and Risk-driven Testing.","author":"L. Krautsevich","year":"2013","unstructured":"L. Krautsevich et almbox. 2013 . Towards attribute-based access control policy engineering using risk . In Intl. Workshop on Risk Assessment and Risk-driven Testing. L. Krautsevich et almbox. 2013. Towards attribute-based access control policy engineering using risk. In Intl. Workshop on Risk Assessment and Risk-driven Testing."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"crossref","unstructured":"A. Liu X. Du and N. Wang. 2021. Efficient Access Control Permission Decision Engine Based on Machine Learning. Security & Communication Networks (2021).  A. Liu X. Du and N. Wang. 2021. Efficient Access Control Permission Decision Engine Based on Machine Learning. Security & Communication Networks (2021).","DOI":"10.1155\/2021\/3970485"},{"key":"e_1_3_2_2_48_1","unstructured":"A. Madry A. Makelov L. Schmidt D. Tsipras and A. Vladu. 2017. Towards deep learning models resistant to adversarial attacks. arXiv:1706.06083 (2017).  A. Madry A. Makelov L. Schmidt D. Tsipras and A. Vladu. 2017. Towards deep learning models resistant to adversarial attacks. arXiv:1706.06083 (2017)."},{"key":"e_1_3_2_2_49_1","volume-title":"Evolutionary inference of attribute-based access control policies","author":"Medvet Eric","unstructured":"Eric Medvet , Alberto Bartoli , Barbara Carminati , and Elena Ferrari . 2015. Evolutionary inference of attribute-based access control policies . In EMO. Springer . Eric Medvet, Alberto Bartoli, Barbara Carminati, and Elena Ferrari. 2015. Evolutionary inference of attribute-based access control policies. In EMO. Springer."},{"key":"e_1_3_2_2_50_1","volume-title":"mbox","author":"N. Mehrabi","year":"2019","unstructured":"N. Mehrabi et al mbox . 2019 . A survey on bias and fairness in machine learning. arXiv. N. Mehrabi et almbox. 2019. A survey on bias and fairness in machine learning. arXiv."},{"key":"e_1_3_2_2_51_1","volume-title":"mbox","author":"B. Mitra","year":"2016","unstructured":"B. Mitra et al mbox . 2016 . A survey of role mining. Computing Surveys (CSUR) ( 2016). B. Mitra et almbox. 2016. A survey of role mining. Computing Surveys (CSUR) (2016)."},{"key":"e_1_3_2_2_52_1","unstructured":"D. Mocanu F. Turkmen and A. Liotta. 2015. Towards ABAC policy mining from logs with deep learning. In International Multiconference (Intelligent Systems).  D. Mocanu F. Turkmen and A. Liotta. 2015. Towards ABAC policy mining from logs with deep learning. In International Multiconference (Intelligent Systems)."},{"key":"e_1_3_2_2_53_1","volume-title":"mbox","author":"Ian Molloy","year":"2009","unstructured":"Ian Molloy et al mbox . 2009 . Evaluating role mining algorithms. In SACMAT. ACM. Ian Molloy et almbox. 2009. Evaluating role mining algorithms. In SACMAT. ACM."},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/1978672.1978679"},{"key":"e_1_3_2_2_55_1","volume-title":"et almbox","author":"Narouei M.","year":"2017","unstructured":"M. Narouei , H. Khanpour , H. Takabi , et almbox . 2017 . Towards a top-down policy engineering framework for attribute-based access control. In SACMAT. ACM. M. Narouei, H. Khanpour, H. Takabi, et almbox. 2017. Towards a top-down policy engineering framework for attribute-based access control. In SACMAT. ACM."},{"key":"e_1_3_2_2_56_1","volume-title":"A Nature-Inspired Framework for Optimal Mining of Attribute-Based Access Control Policies","author":"Narouei Masoud","unstructured":"Masoud Narouei and Hassan Takabi . 2019. A Nature-Inspired Framework for Optimal Mining of Attribute-Based Access Control Policies . In ICSPCS. Springer . Masoud Narouei and Hassan Takabi. 2019. A Nature-Inspired Framework for Optimal Mining of Attribute-Based Access Control Policies. In ICSPCS. Springer."},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"crossref","unstructured":"A. Nguyen J. Yosinski and J. Clune. 2015. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In CVPR. IEEE.  A. Nguyen J. Yosinski and J. Clune. 2015. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In CVPR. IEEE.","DOI":"10.1109\/CVPR.2015.7298640"},{"key":"e_1_3_2_2_58_1","volume-title":"C. Kanan, and S. Wermter.","author":"Parisi G. I","year":"2019","unstructured":"G. I Parisi , R. Kemker , J. L Part , C. Kanan, and S. Wermter. 2019 . Continual lifelong learning with neural networks: A review. Neural Networks ( 2019). G. I Parisi, R. Kemker, J. L Part, C. Kanan, and S. Wermter. 2019. Continual lifelong learning with neural networks: A review. Neural Networks (2019)."},{"key":"e_1_3_2_2_59_1","volume-title":"mbox","author":"F. Pedregosa","year":"2011","unstructured":"F. Pedregosa et al mbox . 2011 . Scikit-learn : Machine Learning in Python. JMLR. F. Pedregosa et almbox. 2011. Scikit-learn: Machine Learning in Python. JMLR."},{"key":"e_1_3_2_2_60_1","volume-title":"Research and Application of Rigorous Access Control Mechanism in Distributed Objects System","author":"Sainan Zhang","unstructured":"Zhang Sainan and Zheng Changyou . 2019. Research and Application of Rigorous Access Control Mechanism in Distributed Objects System . In ITNEC. IEEE. Zhang Sainan and Zheng Changyou. 2019. Research and Application of Rigorous Access Control Mechanism in Distributed Objects System. In ITNEC. IEEE."},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"crossref","unstructured":"Matthew W Sanders and Chuan Yue. 2019. Mining least privilege attribute based access control policies. In ACSAC.  Matthew W Sanders and Chuan Yue. 2019. Mining least privilege attribute based access control policies. In ACSAC.","DOI":"10.1145\/3359789.3359805"},{"key":"e_1_3_2_2_62_1","volume-title":"mbox","author":"Ravi Sandhu","year":"1996","unstructured":"Ravi Sandhu et al mbox . 1996 . Role-based access control models. Computer ( 1996). Ravi Sandhu et almbox. 1996. Role-based access control models. Computer (1996)."},{"key":"e_1_3_2_2_63_1","unstructured":"Gerhard Schimpf. 2000. Role-engineering critical success factors for enterprise security administration. In ACSAC. ACM.  Gerhard Schimpf. 2000. Role-engineering critical success factors for enterprise security administration. In ACSAC. ACM."},{"key":"e_1_3_2_2_64_1","volume-title":"Deep learning in neural networks: An overview. Neural networks","author":"Schmidhuber J\u00fcrgen","year":"2015","unstructured":"J\u00fcrgen Schmidhuber . 2015. Deep learning in neural networks: An overview. Neural networks , Vol. 61 ( 2015 ). J\u00fcrgen Schmidhuber. 2015. Deep learning in neural networks: An overview. Neural networks, Vol. 61 (2015)."},{"key":"e_1_3_2_2_65_1","unstructured":"Cedric Seger. 2018. An investigation of categorical variable encoding techniques in machine learning: binary versus one-hot and feature hashing.  Cedric Seger. 2018. An investigation of categorical variable encoding techniques in machine learning: binary versus one-hot and feature hashing."},{"key":"e_1_3_2_2_66_1","unstructured":"Avanti Shrikumar Peyton Greenside and Anshul Kundaje. 2017. Learning important features through propagating activation differences. In ICML. PMLR.  Avanti Shrikumar Peyton Greenside and Anshul Kundaje. 2017. Learning important features through propagating activation differences. In ICML. PMLR."},{"key":"e_1_3_2_2_67_1","volume-title":"Insider Attack and Cyber Security","author":"Sinclair Sara","unstructured":"Sara Sinclair and Sean W Smith . 2008. Preventative directions for insider threat mitigation via access control . In Insider Attack and Cyber Security . Springer . Sara Sinclair and Sean W Smith. 2008. Preventative directions for insider threat mitigation via access control. In Insider Attack and Cyber Security. Springer."},{"key":"e_1_3_2_2_68_1","volume-title":"mbox","author":"M. Sundararajan","year":"2017","unstructured":"M. Sundararajan et al mbox . 2017 . Axiomatic attribution for deep networks. In ICML. M. Sundararajan et almbox. 2017. Axiomatic attribution for deep networks. In ICML."},{"key":"e_1_3_2_2_69_1","volume-title":"Intl. Conference on Collaboration and Internet Computing. IEEE.","author":"Tanay","unstructured":"Tanay Talukdar et almbox. 2017. Efficient bottom-up mining of attribute based access control policies . In Intl. Conference on Collaboration and Internet Computing. IEEE. Tanay Talukdar et almbox. 2017. Efficient bottom-up mining of attribute based access control policies. In Intl. Conference on Collaboration and Internet Computing. IEEE."},{"key":"e_1_3_2_2_70_1","volume-title":"V d Maaten and G. Hinton","author":"L.","year":"2008","unstructured":"L. V d Maaten and G. Hinton . 2008 . Visualizing data using t-SNE. JMLR ( 2008). L. V d Maaten and G. Hinton. 2008. Visualizing data using t-SNE. JMLR (2008)."},{"key":"e_1_3_2_2_71_1","volume-title":"mbox","author":"J. Vaidya","year":"2010","unstructured":"J. Vaidya et al mbox . 2010 . The role mining problem: A formal perspective. TISSEC. J. Vaidya et almbox. 2010. The role mining problem: A formal perspective. TISSEC."},{"key":"e_1_3_2_2_72_1","doi-asserted-by":"publisher","DOI":"10.5555\/2449288.2449295"},{"key":"e_1_3_2_2_73_1","volume-title":"et almbox","author":"Xiang Chengcheng","year":"2019","unstructured":"Chengcheng Xiang , Yudong Wu , et almbox . 2019 . Towards Continuous Access Control Validation and Forensics. In CCS. ACM. Chengcheng Xiang, Yudong Wu, et almbox. 2019. Towards Continuous Access Control Validation and Forensics. In CCS. ACM."},{"key":"e_1_3_2_2_74_1","doi-asserted-by":"crossref","unstructured":"Z. Xu and S. D Stoller. 2012. Algorithms for mining meaningful roles. In SACMAT.  Z. Xu and S. D Stoller. 2012. Algorithms for mining meaningful roles. In SACMAT.","DOI":"10.1145\/2295136.2295146"},{"key":"e_1_3_2_2_75_1","volume-title":"Mining attribute-based access control policies. TDSC","author":"Xu Zhongyuan","year":"2014","unstructured":"Zhongyuan Xu and Scott D Stoller . 2014a. Mining attribute-based access control policies. TDSC ( 2014 ). Zhongyuan Xu and Scott D Stoller. 2014a. Mining attribute-based access control policies. TDSC (2014)."},{"key":"e_1_3_2_2_76_1","volume-title":"DBSec","author":"Xu Zhongyuan","unstructured":"Zhongyuan Xu and Scott D Stoller . 2014b. Mining attribute-based access control policies from logs . In DBSec . Springer . Zhongyuan Xu and Scott D Stoller. 2014b. Mining attribute-based access control policies from logs. In DBSec. Springer."},{"key":"e_1_3_2_2_77_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33012253"}],"event":{"name":"CODASPY '22: Twelveth ACM Conference on Data and Application Security and Privacy","location":"Baltimore MD USA","acronym":"CODASPY '22","sponsor":["SIGSAC ACM Special Interest Group on Security, Audit, and Control"]},"container-title":["Proceedings of the Twelfth ACM Conference on Data and Application Security and Privacy"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3508398.3511497","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3508398.3511497","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:49:37Z","timestamp":1750182577000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3508398.3511497"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,14]]},"references-count":77,"alternative-id":["10.1145\/3508398.3511497","10.1145\/3508398"],"URL":"https:\/\/doi.org\/10.1145\/3508398.3511497","relation":{},"subject":[],"published":{"date-parts":[[2022,4,14]]},"assertion":[{"value":"2022-04-15","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}