{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T16:39:16Z","timestamp":1770223156982,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":50,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T00:00:00Z","timestamp":1638748800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["30918012204,30920041112"],"award-info":[{"award-number":["30918012204,30920041112"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["2122631 (1814825), 2115134"],"award-info":[{"award-number":["2122631 (1814825), 2115134"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,12,6]]},"DOI":"10.1145\/3485832.3485916","type":"proceedings-article","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T13:42:32Z","timestamp":1638798152000},"page":"596-608","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Can We Leverage Predictive Uncertainty to Detect Dataset Shift and Adversarial Examples in Android Malware Detection?"],"prefix":"10.1145","author":[{"given":"Deqiang","family":"Li","sequence":"first","affiliation":[{"name":"Nanjing University of Science and Technology, China"}]},{"given":"Tian","family":"Qiu","sequence":"additional","affiliation":[{"name":"Nanjing University of Science and Technology, China"}]},{"given":"Shuo","family":"Chen","sequence":"additional","affiliation":[{"name":"RIKEN, Japan"}]},{"given":"Qianmu","family":"Li","sequence":"additional","affiliation":[{"name":"Nanjing University of Science and Technology, China"}]},{"given":"Shouhuai","family":"Xu","sequence":"additional","affiliation":[{"name":"University of Colorado Colorado Springs, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,12,6]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Mart\u00edn Abadi Ashish Agarwal and et al.2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https:\/\/www.tensorflow.org\/ Software available from tensorflow.org."},{"key":"e_1_3_2_1_2_1","volume-title":"Tensorflow: A system for large-scale machine learning. In OSDI","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi, Paul Barham, 2016. Tensorflow: A system for large-scale machine learning. In OSDI\u2019 16. USENIX Association, Savannah, GA, USA, 265\u2013283."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2901739.2903508"},{"key":"e_1_3_2_1_4_1","volume-title":"Drebin: Effective and explainable detection of android malware in your pocket. In NDSS, Vol.\u00a014","author":"Arp Daniel","year":"2014","unstructured":"Daniel Arp, Michael Spreitzenbarth, 2014. Drebin: Effective and explainable detection of android malware in your pocket. In NDSS, Vol.\u00a014. The Internet Society, San Diego, California, USA, 23\u201326."},{"key":"e_1_3_2_1_5_1","unstructured":"Umang Bhatt Yunfeng Zhang and et al. 2020. Uncertainty as a Form of Transparency: Measuring Communicating and Using Uncertainty. CoRR abs\/2011.07586(2020). https:\/\/arxiv.org\/abs\/2011.07586"},{"key":"e_1_3_2_1_6_1","volume-title":"Proceedings of the 32nd International Conference on Machine Learning, Vol.\u00a037","author":"Blundell Charles","year":"2015","unstructured":"Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra. 2015. Weight Uncertainty in Neural Network. In Proceedings of the 32nd International Conference on Machine Learning, Vol.\u00a037. PMLR, Lille, France, 1613\u20131622. https:\/\/proceedings.mlr.press\/v37\/blundell15.html"},{"key":"e_1_3_2_1_7_1","volume-title":"Verification of forecasts expressed in terms of probability. Monthly weather review 78, 1","author":"Brier W","year":"1950","unstructured":"Glenn\u00a0W Brier. 1950. Verification of forecasts expressed in terms of probability. Monthly weather review 78, 1 (1950), 1\u20133."},{"key":"e_1_3_2_1_8_1","volume-title":"The Balanced Accuracy and Its Posterior Distribution. In 2010 20th International Conference on Pattern Recognition. IEEE Computer Society","author":"Brodersen H.","year":"2010","unstructured":"K.\u00a0H. Brodersen, C.\u00a0S. Ong, K.\u00a0E. Stephan, and J.\u00a0M. Buhmann. 2010. The Balanced Accuracy and Its Posterior Distribution. In 2010 20th International Conference on Pattern Recognition. IEEE Computer Society, Istanbul, Turkey, 3121\u20133124."},{"key":"e_1_3_2_1_9_1","volume-title":"First PASCAL Machine Learning Challenges Workshop, Vol.\u00a03944","author":"Candela Joaquin\u00a0Qui\u00f1onero","year":"2005","unstructured":"Joaquin\u00a0Qui\u00f1onero Candela, Carl\u00a0Edward Rasmussen, Fabian\u00a0H. Sinz, Olivier Bousquet, and Bernhard Sch\u00f6lkopf. 2005. Evaluating Predictive Uncertainty Challenge. In Machine Learning Challenges, Evaluating Predictive Uncertainty, Visual Object Classification and Recognizing Textual Entailment, First PASCAL Machine Learning Challenges Workshop, Vol.\u00a03944. Springer, Southampton, UK, 1\u201327."},{"key":"e_1_3_2_1_10_1","volume-title":"EISIC\u20192017","author":"Chen Lingwei","unstructured":"Lingwei Chen, Yanfang Ye, and Thirimachos Bourlai. 2017. Adversarial Machine Learning in Malware Detection: Arms Race between Evasion Attack and Defense. In EISIC\u20192017. IEEE Computer Society, Athens, Greece, 99\u2013106."},{"key":"e_1_3_2_1_11_1","unstructured":"Forensics Corvus. 2020. VirusShare. https:\/\/virusshare.com\/"},{"key":"e_1_3_2_1_12_1","first-page":"711","article-title":"Yes, machine learning can be more secure! a case study on android malware detection","volume":"16","author":"Demontis Ambra","year":"2017","unstructured":"Ambra Demontis, Marco Melis, 2017. Yes, machine learning can be more secure! a case study on android malware detection. IEEE TDSC 16, 4 (2017), 711\u2013724.","journal-title":"IEEE TDSC"},{"key":"e_1_3_2_1_13_1","unstructured":"Anthony Desnos. 2020. Androguard. https:\/\/github.com\/androguard\/androguard"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2018.2833292"},{"key":"e_1_3_2_1_15_1","volume-title":"international conference on machine learning. JMLR.org, NY, USA, 1050\u20131059","author":"Gal Yarin","year":"2016","unstructured":"Yarin Gal and Zoubin Ghahramani. 2016. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning. JMLR.org, NY, USA, 1050\u20131059."},{"key":"e_1_3_2_1_16_1","volume-title":"Practical Variational Inference for Neural Networks. In Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems","author":"Graves Alex","year":"2011","unstructured":"Alex Graves. 2011. Practical Variational Inference for Neural Networks. In Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Curran Associates Inc., Granada, Spain, 2348\u20132356."},{"key":"e_1_3_2_1_17_1","volume-title":"Adversarial examples for malware detection","author":"Grosse Kathrin","unstructured":"Kathrin Grosse, Nicolas Papernot, 2017. Adversarial examples for malware detection. In ESORICS. Springer, Oslo, Norway, 62\u201379."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.5555\/3305381.3305518"},{"key":"e_1_3_2_1_19_1","volume-title":"2018 IEEE International Conference on Big Data (Big Data). IEEE","author":"Huang H.","unstructured":"T.\u00a0H. Huang and H. Kao. 2018. R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections. In 2018 IEEE International Conference on Big Data (Big Data). IEEE, Seattle, WA, USA, 2633\u20132642."},{"key":"e_1_3_2_1_20_1","volume-title":"Transcend: Detecting Concept Drift in Malware Classification Models. In USENIX Security 17","author":"Jordaney Roberto","year":"2017","unstructured":"Roberto Jordaney, Kumar Sharad, 2017. Transcend: Detecting Concept Drift in Malware Classification Models. In USENIX Security 17. USENIX Association, Vancouver, BC, 625\u2013642. https:\/\/www.usenix.org\/conference\/usenixsecurity17\/technical-sessions\/presentation\/jordaney"},{"key":"e_1_3_2_1_21_1","volume-title":"What uncertainties do we need in bayesian deep learning for computer vision?","author":"Kendall Alex","unstructured":"Alex Kendall and Yarin Gal. 2017. What uncertainties do we need in bayesian deep learning for computer vision?. In NeurIPS. Curran Associates Inc., Long Beach, CA, USA, 5574\u20135584."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2018.2866319"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1181"},{"key":"e_1_3_2_1_24_1","unstructured":"Kaspersky Lab. 2020. Kaspersky. https:\/\/www.kaspersky.com"},{"key":"e_1_3_2_1_25_1","volume-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","author":"Lakshminarayanan Balaji","unstructured":"Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. 2017. Simple and scalable predictive uncertainty estimation using deep ensembles. In NeurIPS. Curran Associates Inc., Long Beach, CA, USA, 6402\u20136413."},{"key":"e_1_3_2_1_26_1","volume-title":"Leveraging uncertainty information from deep neural networks for disease detection. Scientific reports 7, 1","author":"Leibig Christian","year":"2017","unstructured":"Christian Leibig, Vaneeda Allken, Murat\u00a0Se\u00e7kin Ayhan, Philipp Berens, and Siegfried Wahl. 2017. Leveraging uncertainty information from deep neural networks for disease detection. Scientific reports 7, 1 (2017), 1\u201314."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.3003571"},{"key":"e_1_3_2_1_28_1","first-page":"2346","article-title":"Learning under Concept Drift","volume":"31","author":"Lu Jie","year":"2019","unstructured":"Jie Lu, Anjin Liu, Fan Dong, Feng Gu, Jo\u00e3o Gama, and Guangquan Zhang. 2019. Learning under Concept Drift: A Review. IEEE Trans. Knowl. Data Eng. 31, 12 (2019), 2346\u20132363.","journal-title":"A Review. IEEE Trans. Knowl. Data Eng."},{"key":"e_1_3_2_1_29_1","volume-title":"Droidetec: Android malware detection and malicious code localization through deep learning. CoRR abs\/2002.03594(2020). https:\/\/arxiv.org\/abs\/2002.03594","author":"Ma Zhuo","year":"2020","unstructured":"Zhuo Ma, Haoran Ge, Zhuzhu Wang, Yang Liu, and Ximeng Liu. 2020. Droidetec: Android malware detection and malicious code localization through deep learning. CoRR abs\/2002.03594(2020). https:\/\/arxiv.org\/abs\/2002.03594"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3029806.3029823"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.5555\/2888116.2888120"},{"key":"e_1_3_2_1_32_1","unstructured":"Andr\u00e9\u00a0T. Nguyen Edward Raff Charles Nicholas and James Holt. 2021. Leveraging Uncertainty for Improved Static Malware Detection Under Extreme False Positive Constraints. CoRR abs\/2108.04081(2021). https:\/\/arxiv.org\/abs\/2108.04081"},{"key":"e_1_3_2_1_33_1","volume-title":"ICML","author":"Niculescu-Mizil Alexandru","unstructured":"Alexandru Niculescu-Mizil and Rich Caruana. 2005. Predicting good probabilities with supervised learning. In ICML. ACM, Bonn, Germany, 625\u2013632."},{"key":"e_1_3_2_1_34_1","unstructured":"Tim Pearce Felix Leibfried 2020. Uncertainty in Neural Networks: Approximately Bayesian Ensembling. In AISTATS. PMLR Online [Palermo Sicily Italy] 234\u2013244."},{"key":"e_1_3_2_1_35_1","volume-title":"TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time. In USENIX Security 19","author":"Pendlebury Feargus","year":"2019","unstructured":"Feargus Pendlebury, Fabio Pierazzi, 2019. TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time. In USENIX Security 19. USENIX Association, Santa Clara, CA, 729\u2013746. https:\/\/www.usenix.org\/conference\/usenixsecurity19\/presentation\/pendlebury"},{"key":"e_1_3_2_1_36_1","first-page":"4","article-title":"A Survey on Systems Security Metrics","volume":"49","author":"Pendleton Marcus","year":"2016","unstructured":"Marcus Pendleton, Richard Garcia-Lebron, Jin-Hee Cho, and Shouhuai Xu. 2016. A Survey on Systems Security Metrics. ACM Comput. Surv. 49, 4 (Dec. 2016), 1\u201335.","journal-title":"ACM Comput. Surv."},{"key":"e_1_3_2_1_37_1","volume-title":"Dataset Shift in Machine Learning","author":"Quionero-Candela Joaquin","unstructured":"Joaquin Quionero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil\u00a0D. Lawrence. 2009. Dataset Shift in Machine Learning. The MIT Press, Cambridge, MA."},{"key":"e_1_3_2_1_38_1","unstructured":"Hispasec Sistemas. 2020. VirusTotal. Alphabet Inc. https:\/\/www.virustotal.com"},{"key":"e_1_3_2_1_39_1","volume-title":"Advances in Neural Information Processing Systems","author":"Snoek Jasper","unstructured":"Jasper Snoek, Yaniv Ovadia, Emily Fertig, Balaji Lakshminarayanan, Sebastian Nowozin, D Sculley, Joshua Dillon, Jie Ren, and Zachary Nado. 2019. Can you trust your model\u2019s uncertainty? Evaluating predictive uncertainty under dataset shift. In Advances in Neural Information Processing Systems. Curran Associates Inc., Vancouver, BC, Canada, 13969\u201313980."},{"key":"e_1_3_2_1_40_1","volume-title":"Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15, 1","author":"Srivastava Nitish","year":"2014","unstructured":"Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15, 1 (2014), 1929\u20131958."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/tcbb.2007.1015"},{"key":"e_1_3_2_1_42_1","unstructured":"Connor Tumbleson and Ryszard Wi\u015bniewski. 2020. Apktool. https:\/\/ibotpeaches.github.io\/Apktool"},{"key":"e_1_3_2_1_43_1","volume-title":"The 22nd International Conference on Artificial Intelligence and Statistics, AISTATS, Vol.\u00a089","author":"Vaicenavicius Juozas","year":"2019","unstructured":"Juozas Vaicenavicius, David Widmann, Carl\u00a0R. Andersson, Fredrik Lindsten, Jacob Roll, and Thomas\u00a0B. Sch\u00f6n. 2019. Evaluating model calibration in classification. In The 22nd International Conference on Artificial Intelligence and Statistics, AISTATS, Vol.\u00a089. PMLR, Naha, Okinawa, Japan, 3459\u20133467."},{"key":"e_1_3_2_1_44_1","volume-title":"Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs. In 9th International Conference on Learning Representations. OpenReview.net","author":"Wang Cheng","year":"2021","unstructured":"Cheng Wang, Carolin Lawrence, and Mathias Niepert. 2021. Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs. In 9th International Conference on Learning Representations. OpenReview.net, Virtual Event, Austria."},{"key":"e_1_3_2_1_45_1","volume-title":"Bayesian learning via stochastic gradient Langevin dynamics","author":"Welling Max","unstructured":"Max Welling and Yee\u00a0W Teh. 2011. Bayesian learning via stochastic gradient Langevin dynamics. In ICML. Omnipress, Madison, WI, USA, 681\u2013688."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1018046501280"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/CNS.2014.6997494"},{"key":"e_1_3_2_1_48_1","volume-title":"2017. A Survey on Malware Detection Using Data Mining Techniques. ACM Comput. Surv. 50, 3","author":"Ye Yanfang","year":"2017","unstructured":"Yanfang Ye, Tao Li, and et al.2017. A Survey on Malware Detection Using Data Mining Techniques. ACM Comput. Surv. 50, 3 (2017), 41:1\u201341:40."},{"key":"e_1_3_2_1_49_1","volume-title":"CCS 2020","author":"Zhang Xiaohan","unstructured":"Xiaohan Zhang, Yuan Zhang, and et al.2020. Enhancing State-of-the-Art Classifiers with API Semantics to Detect Evolved Android Malware. In CCS 2020 (Virtual Event, USA). Association for Computing Machinery, New York, USA, 757\u2013770."},{"key":"e_1_3_2_1_50_1","volume-title":"An Overview of Concept Drift Applications","author":"\u017dliobait\u0117 Indr\u0117","unstructured":"Indr\u0117 \u017dliobait\u0117, Mykola Pechenizkiy, and Jo\u00e3o Gama. 2016. An Overview of Concept Drift Applications. Springer International Publishing, Cham, 91\u2013114."}],"event":{"name":"ACSAC '21: Annual Computer Security Applications Conference","location":"Virtual Event USA","acronym":"ACSAC '21"},"container-title":["Annual Computer Security Applications Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3485832.3485916","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3485832.3485916","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3485832.3485916","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T19:16:25Z","timestamp":1755890185000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3485832.3485916"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,6]]},"references-count":50,"alternative-id":["10.1145\/3485832.3485916","10.1145\/3485832"],"URL":"https:\/\/doi.org\/10.1145\/3485832.3485916","relation":{},"subject":[],"published":{"date-parts":[[2021,12,6]]},"assertion":[{"value":"2021-12-06","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}