{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T17:11:32Z","timestamp":1772039492391,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":106,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005911","name":"Najran University","doi-asserted-by":"publisher","award":["443-16-1171"],"award-info":[{"award-number":["443-16-1171"]}],"id":[{"id":"10.13039\/501100005911","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,9,30]]},"DOI":"10.1145\/3678890.3678901","type":"proceedings-article","created":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T22:23:36Z","timestamp":1727648616000},"page":"215-234","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Mateen: Adaptive Ensemble Learning for Network Anomaly Detection"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2901-5313","authenticated-orcid":false,"given":"Fahad","family":"Alotaibi","sequence":"first","affiliation":[{"name":"Department of Computing, Imperial College London, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1514-6857","authenticated-orcid":false,"given":"Sergio","family":"Maffeis","sequence":"additional","affiliation":[{"name":"Department of Computing, Imperial College London, United Kingdom"}]}],"member":"320","published-online":{"date-parts":[[2024,9,30]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP57164.2023.00043"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-22390-7_18"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-88942-5_9"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3474369.3486864"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2007.190645"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP57164.2023.00042"},{"key":"e_1_3_2_2_7_1","volume-title":"Proc. of USENIX Security.","author":"Arp Daniel","year":"2022","unstructured":"Daniel Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro, and Konrad Rieck. 2022. Dos and Don\u2019ts of Machine Learning in Computer Security. In Proc. of USENIX Security."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSEC.2023.3287207"},{"key":"e_1_3_2_2_9_1","volume-title":"Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook","author":"Bank Dor","year":"2023","unstructured":"Dor Bank, Noam Koenigstein, and Raja Giryes. 2023. Autoencoders. Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook (2023), 353\u2013374."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833659"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972771.42"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15880-3_15"},{"key":"e_1_3_2_2_13_1","volume-title":"Assessing and Improving Malware Detection Sustainability through App Evolution Studies. ACM Transactions on Software Engineering and Methodology","author":"Cai Haipeng","year":"2020","unstructured":"Haipeng Cai. 2020. Assessing and Improving Malware Detection Sustainability through App Evolution Studies. ACM Transactions on Software Engineering and Methodology (2020)."},{"key":"e_1_3_2_2_14_1","volume-title":"Proc. of NeurIPS","author":"Chen Jian","year":"2024","unstructured":"Jian Chen, Ruiyi Zhang, Tong Yu, Rohan Sharma, Zhiqiang Xu, Tong Sun, and Changyou Chen. 2024. Label-retrieval-augmented diffusion models for learning from noisy labels. Proc. of NeurIPS (2024)."},{"key":"e_1_3_2_2_15_1","volume-title":"Proc. of ICML.","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In Proc. of ICML."},{"key":"e_1_3_2_2_16_1","volume-title":"Proc. of USENIX Security.","author":"Chen Yizheng","year":"2023","unstructured":"Yizheng Chen, Zhoujie Ding, and David\u00a0A. Wagner. 2023. Continuous Learning for Android Malware Detection. In Proc. of USENIX Security."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3100509"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/198"},{"key":"e_1_3_2_2_19_1","volume-title":"Proc. of ICLR.","author":"Coleman Cody","year":"2020","unstructured":"Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, and Matei Zaharia. 2020. Selection via Proxy: Efficient Data Selection for Deep Learning. In Proc. of ICLR."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR56361.2022.9956240"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3597926.3598054"},{"key":"e_1_3_2_2_22_1","volume-title":"Proc. of ESORICS.","author":"Dias Tiago\u00a0Fontes","year":"2023","unstructured":"Tiago\u00a0Fontes Dias, Jo\u00e3o Vitorino, Tiago Fonseca, Isabel Pra\u00e7a, Eva Maia, and Maria\u00a0Jo\u00e3o Viamonte. 2023. Unravelling Network-Based Intrusion Detection: A Neutrosophic Rule Mining and Optimization Framework. In Proc. of ESORICS."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3488932.3517393"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8851731"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2012.136"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.5220\/0005740704070414"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3319535.3363226"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2011.2160459"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/SPW53761.2021.00009"},{"key":"e_1_3_2_2_30_1","volume-title":"An adaptive ensemble classifier for mining concept drifting data streams. Expert Systems with Applications","author":"Farid Md.","year":"2013","unstructured":"Dewan\u00a0Md. Farid, Li Zhang, M.\u00a0Alamgir Hossain, Chowdhury\u00a0Mofizur Rahman, Rebecca Strachan, Graham Sexton, and Keshav\u00a0P. Dahal. 2013. An adaptive ensemble classifier for mining concept drifting data streams. Expert Systems with Applications (2013)."},{"key":"e_1_3_2_2_31_1","volume-title":"Two-sample Kolmogorov\u2013Smirnov-type tests revisited: old and new tests in terms of local levels. The Annals of Statistics","author":"Finner Helmut","year":"2018","unstructured":"Helmut Finner and Veronika Gontscharuk. 2018. Two-sample Kolmogorov\u2013Smirnov-type tests revisited: old and new tests in terms of local levels. The Annals of Statistics (2018)."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW63382.2024.00405"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3576915.3616631"},{"key":"e_1_3_2_2_34_1","volume-title":"On evaluating stream learning algorithms. Machine learning","author":"Gama Jo\u00e3o","year":"2013","unstructured":"Jo\u00e3o Gama, Raquel Sebasti\u00e3o, and Pedro\u00a0Pereira Rodrigues. 2013. On evaluating stream learning algorithms. Machine learning (2013)."},{"key":"e_1_3_2_2_35_1","volume-title":"A survey on concept drift adaptation. Comput. Surveys","author":"Gama Jo\u00e3o","year":"2014","unstructured":"Jo\u00e3o Gama, Indre Zliobaite, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia. 2014. A survey on concept drift adaptation. Comput. Surveys (2014)."},{"key":"e_1_3_2_2_36_1","volume-title":"Proc. of ESORICS.","author":"Giagkos Dimitrios","year":"2023","unstructured":"Dimitrios Giagkos, Orestis Kompougias, Antonis Litke, and Nikolaos Papadakis. 2023. ZeekFlow: Deep Learning-Based Network Intrusion Detection a Multimodal Approach. In Proc. of ESORICS."},{"key":"e_1_3_2_2_37_1","volume-title":"Datasets are not enough: Challenges in labeling network traffic. Computers & Security","author":"Guerra Jorge\u00a0Luis","year":"2022","unstructured":"Jorge\u00a0Luis Guerra, Carlos Catania, and Eduardo Veas. 2022. Datasets are not enough: Challenges in labeling network traffic. Computers & Security (2022)."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2006.100"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2023.24830"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3460120.3484589"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3087242"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3607199.3607206"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3359992.3366642"},{"key":"e_1_3_2_2_44_1","volume-title":"Learning concept drift with ensembles of optimum-path forest-based classifiers. Future Generation Computer Systems","author":"Iwashita S.","year":"2019","unstructured":"Adriana\u00a0S. Iwashita, Victor Hugo\u00a0C. de Albuquerque, and Jo\u00e3o\u00a0Paulo Papa. 2019. Learning concept drift with ensembles of optimum-path forest-based classifiers. Future Generation Computer Systems (2019)."},{"key":"e_1_3_2_2_45_1","volume-title":"Proc. of USENIX Security.","author":"Jordaney Roberto","year":"2017","unstructured":"Roberto Jordaney, Kumar Sharad, Santanu\u00a0Kumar Dash, Zhi Wang, Davide Papini, Ilia Nouretdinov, and Lorenzo Cavallaro. 2017. Transcend: Detecting Concept Drift in Malware Classification Models. In Proc. of USENIX Security."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMPSAC.2019.00017"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3474369.3486873"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-80825-9_16"},{"key":"e_1_3_2_2_49_1","volume-title":"Proc. of NeurIPS","author":"Khosla Prannay","year":"2020","unstructured":"Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. 2020. Supervised contrastive learning. Proc. of NeurIPS (2020)."},{"key":"e_1_3_2_2_50_1","volume-title":"Proc. of ICML.","author":"Killamsetty Krishnateja","year":"2021","unstructured":"Krishnateja Killamsetty, Sivasubramanian Durga, Ganesh Ramakrishnan, Abir De, and Rishabh Iyer. 2021. Grad-match: Gradient matching based data subset selection for efficient deep model training. In Proc. of ICML."},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i9.16988"},{"key":"e_1_3_2_2_52_1","volume-title":"Proc. of AISTATS.","author":"Kloft Marius","year":"2010","unstructured":"Marius Kloft and Pavel Laskov. 2010. Online Anomaly Detection under Adversarial Impact. In Proc. of AISTATS."},{"key":"e_1_3_2_2_53_1","volume-title":"FCSCNN: Feature centralized Siamese CNN-based android malware identification. Computers & Security","author":"Kong Ke","year":"2022","unstructured":"Ke Kong, Zhichao Zhang, Ziyuan Yang, and Zhaoxin Zhang. 2022. FCSCNN: Feature centralized Siamese CNN-based android malware identification. Computers & Security (2022)."},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3339252.3339266"},{"key":"e_1_3_2_2_55_1","volume-title":"Learn to adapt: Robust drift detection in security domain. Computers and Electrical Engineering","author":"Kuppa Aditya","year":"2022","unstructured":"Aditya Kuppa and Nhien-An Le-Khac. 2022. Learn to adapt: Robust drift detection in security domain. Computers and Electrical Engineering (2022)."},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3607199.3607247"},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"publisher","DOI":"10.5220\/0006105602530262"},{"key":"e_1_3_2_2_58_1","volume-title":"Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of experimental social psychology","author":"Leys Christophe","year":"2013","unstructured":"Christophe Leys, Christophe Ley, Olivier Klein, Philippe Bernard, and Laurent Licata. 2013. Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of experimental social psychology (2013)."},{"key":"e_1_3_2_2_59_1","volume-title":"Proc. of NeurIPS","author":"Li Ruoyu","year":"2024","unstructured":"Ruoyu Li, Qing Li, Yu Zhang, Dan Zhao, Yong Jiang, and Yong Yang. 2024. Interpreting Unsupervised Anomaly Detection in Security via Rule Extraction. Proc. of NeurIPS (2024)."},{"key":"e_1_3_2_2_60_1","volume-title":"Building Auto-Encoder Intrusion Detection System based on random forest feature selection. Computers & Security","author":"Li XuKui","year":"2020","unstructured":"XuKui Li, Wei Chen, Qianru Zhang, and Lifa Wu. 2020. Building Auto-Encoder Intrusion Detection System based on random forest feature selection. Computers & Security (2020)."},{"key":"e_1_3_2_2_61_1","volume-title":"Proc. of ICLR.","author":"Li Xiang","year":"2020","unstructured":"Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang. 2020. On the Convergence of FedAvg on Non-IID Data. In Proc. of ICLR."},{"key":"e_1_3_2_2_62_1","volume-title":"Selecting Critical Patterns Based on Local Geometrical and Statistical Information","author":"Li Yuhua","year":"2011","unstructured":"Yuhua Li and Liam\u00a0P. Maguire. 2011. Selecting Critical Patterns Based on Local Geometrical and Statistical Information. IEEE Transactions on Pattern Analysis and Machine Intelligence (2011)."},{"key":"e_1_3_2_2_63_1","volume-title":"Accumulating regional density dissimilarity for concept drift detection in data streams. Pattern Recognition","author":"Liu Anjin","year":"2018","unstructured":"Anjin Liu, Jie Lu, Feng Liu, and Guangquan Zhang. 2018. Accumulating regional density dissimilarity for concept drift detection in data streams. Pattern Recognition (2018)."},{"key":"e_1_3_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/CNS56114.2022.9947235"},{"key":"e_1_3_2_2_65_1","volume-title":"Proc. of NeurIPS","author":"Liu Sheng","year":"2020","unstructured":"Sheng Liu, Jonathan Niles-Weed, Narges Razavian, and Carlos Fernandez-Granda. 2020. Early-learning regularization prevents memorization of noisy labels. Proc. of NeurIPS (2020)."},{"key":"e_1_3_2_2_66_1","volume-title":"Learning under Concept Drift: A Review","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 Transactions on Knowledge and Data Engineering (2019)."},{"key":"e_1_3_2_2_67_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/333"},{"key":"e_1_3_2_2_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2951814"},{"key":"e_1_3_2_2_69_1","volume-title":"Proc. of ICML.","author":"Ma Xingjun","year":"2020","unstructured":"Xingjun Ma, Hanxun Huang, Yisen Wang, Simone Romano, Sarah Erfani, and James Bailey. 2020. Normalized loss functions for deep learning with noisy labels. In Proc. of ICML."},{"key":"e_1_3_2_2_70_1","volume-title":"Some properties of two-sample kolmogorov\u2013smirnov test in the case of contamination of one of the samples. Journal of Mathematical Sciences","author":"Makarov AA","year":"2016","unstructured":"AA Makarov and GI Simonova. 2016. Some properties of two-sample kolmogorov\u2013smirnov test in the case of contamination of one of the samples. Journal of Mathematical Sciences (2016)."},{"key":"e_1_3_2_2_71_1","volume-title":"A modified Kolmogorov-Smirnov test sensitive to tail alternatives. The Annals of Statistics","author":"Mason M","year":"1983","unstructured":"David\u00a0M Mason and John\u00a0H Schuenemeyer. 1983. A modified Kolmogorov-Smirnov test sensitive to tail alternatives. The Annals of Statistics (1983)."},{"key":"e_1_3_2_2_72_1","volume-title":"Proc. of AISTATS.","author":"McMahan Brendan","year":"2017","unstructured":"Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise\u00a0Ag\u00fcera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proc. of AISTATS."},{"key":"e_1_3_2_2_73_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2018.23204"},{"key":"e_1_3_2_2_74_1","volume-title":"Proc. of ICML.","author":"Mirzasoleiman Baharan","year":"2020","unstructured":"Baharan Mirzasoleiman, Jeff Bilmes, and Jure Leskovec. 2020. Coresets for data-efficient training of machine learning models. In Proc. of ICML."},{"key":"e_1_3_2_2_75_1","volume-title":"A unifying view on dataset shift in classification. Pattern Recognition","author":"Moreno-Torres G.","year":"2012","unstructured":"Jose\u00a0G. Moreno-Torres, Troy Raeder, Roc\u00edo Ala\u00edz-Rodr\u00edguez, Nitesh\u00a0V. Chawla, and Francisco Herrera. 2012. A unifying view on dataset shift in classification. Pattern Recognition (2012)."},{"key":"e_1_3_2_2_76_1","volume-title":"What is being transferred in transfer learning?Proc. of NeurIPS","author":"Neyshabur Behnam","year":"2020","unstructured":"Behnam Neyshabur, Hanie Sedghi, and Chiyuan Zhang. 2020. What is being transferred in transfer learning?Proc. of NeurIPS (2020)."},{"key":"e_1_3_2_2_77_1","doi-asserted-by":"publisher","DOI":"10.1109\/CNS.2019.8802833"},{"key":"e_1_3_2_2_78_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2015.62"},{"key":"e_1_3_2_2_79_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2016.7840667"},{"key":"e_1_3_2_2_80_1","volume-title":"Proc. of NeurIPS","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, 2019. Pytorch: An imperative style, high-performance deep learning library. Proc. of NeurIPS (2019)."},{"key":"e_1_3_2_2_81_1","volume-title":"Proc. of USENIX Security.","author":"Pendlebury Feargus","year":"2019","unstructured":"Feargus Pendlebury, Fabio Pierazzi, Roberto Jordaney, Johannes Kinder, and Lorenzo Cavallaro. 2019. TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time. In Proc. of USENIX Security."},{"key":"e_1_3_2_2_82_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2005013117"},{"key":"e_1_3_2_2_83_1","volume-title":"Proc. of ICML.","author":"Ren Mengye","year":"2018","unstructured":"Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun. 2018. Learning to reweight examples for robust deep learning. In Proc. of ICML."},{"key":"e_1_3_2_2_84_1","volume-title":"A Survey of Deep Active Learning. Comput. Surveys","author":"Ren Pengzhen","year":"2022","unstructured":"Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Brij\u00a0B. Gupta, Xiaojiang Chen, and Xin Wang. 2022. A Survey of Deep Active Learning. Comput. Surveys (2022)."},{"key":"e_1_3_2_2_85_1","volume-title":"Proc. of USENIX Security.","author":"Rieger Phillip","year":"2023","unstructured":"Phillip Rieger, Marco Chilese, Reham Mohamed, Markus Miettinen, Hossein Fereidooni, and Ahmad-Reza Sadeghi. 2023. ARGUS: Context-Based Detection of Stealthy IoT Infiltration Attacks. In Proc. of USENIX Security."},{"key":"e_1_3_2_2_86_1","doi-asserted-by":"publisher","DOI":"10.1145\/3264888.3264890"},{"key":"e_1_3_2_2_87_1","unstructured":"Burr Settles. 2009. Active learning literature survey. (2009)."},{"key":"e_1_3_2_2_88_1","volume-title":"Proc. of NeurIPS","author":"Shafahi Ali","year":"2018","unstructured":"Ali Shafahi, W\u00a0Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein. 2018. Poison frogs! targeted clean-label poisoning attacks on neural networks. Proc. of NeurIPS (2018)."},{"key":"e_1_3_2_2_89_1","doi-asserted-by":"publisher","DOI":"10.5220\/0006639801080116"},{"key":"e_1_3_2_2_90_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2010.25"},{"key":"e_1_3_2_2_91_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155278"},{"key":"e_1_3_2_2_92_1","doi-asserted-by":"publisher","DOI":"10.1145\/3551636"},{"key":"e_1_3_2_2_93_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.082099299"},{"key":"e_1_3_2_2_94_1","volume-title":"Proc. of USENIX Security.","author":"Tong Liang","year":"2019","unstructured":"Liang Tong, Bo Li, Chen Hajaj, Chaowei Xiao, Ning Zhang, and Yevgeniy Vorobeychik. 2019. Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved Features. In Proc. of USENIX Security."},{"key":"e_1_3_2_2_95_1","doi-asserted-by":"publisher","DOI":"10.1145\/3564625.3567992"},{"key":"e_1_3_2_2_96_1","volume-title":"Proc. of ICML.","author":"Wei Hongxin","year":"2023","unstructured":"Hongxin Wei, Huiping Zhuang, Renchunzi Xie, Lei Feng, Gang Niu, Bo An, and Yixuan Li. 2023. Mitigating memorization of noisy labels by clipping the model prediction. In Proc. of ICML."},{"key":"e_1_3_2_2_97_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP46215.2023.10179453"},{"key":"e_1_3_2_2_98_1","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP.2019.00014"},{"key":"e_1_3_2_2_99_1","volume-title":"ADTCD: An Adaptive Anomaly Detection Approach Towards Concept-Drift in IoT","author":"Xu Lijuan","year":"2023","unstructured":"Lijuan Xu, Xiao Ding, Haipeng Peng, Dawei Zhao, and Xin Li. 2023. ADTCD: An Adaptive Anomaly Detection Approach Towards Concept-Drift in IoT. IEEE Internet of Things Journal (2023)."},{"key":"e_1_3_2_2_100_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP46215.2023.10179347"},{"key":"e_1_3_2_2_101_1","volume-title":"Proc. of USENIX Security.","author":"Yang Limin","year":"2021","unstructured":"Limin Yang, Wenbo Guo, Qingying Hao, Arridhana Ciptadi, Ali Ahmadzadeh, Xinyu Xing, and Gang Wang. 2021. CADE: Detecting and Explaining Concept Drift Samples for Security Applications. In Proc. of USENIX Security."},{"key":"e_1_3_2_2_102_1","volume-title":"Proc. of ICML.","author":"Yang Yu","year":"2023","unstructured":"Yu Yang, Hao Kang, and Baharan Mirzasoleiman. 2023. Towards Sustainable Learning: Coresets for Data-efficient Deep Learning. In Proc. of ICML."},{"key":"e_1_3_2_2_103_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539348"},{"key":"e_1_3_2_2_104_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974973.86"},{"key":"e_1_3_2_2_105_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411495.3421359"},{"key":"e_1_3_2_2_106_1","doi-asserted-by":"publisher","DOI":"10.1145\/3372297.3417291"}],"event":{"name":"RAID '24: The 27th International Symposium on Research in Attacks, Intrusions and Defenses","location":"Padua Italy","acronym":"RAID '24"},"container-title":["The 27th International Symposium on Research in Attacks, Intrusions and Defenses"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3678890.3678901","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3678890.3678901","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:00Z","timestamp":1750295880000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3678890.3678901"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,30]]},"references-count":106,"alternative-id":["10.1145\/3678890.3678901","10.1145\/3678890"],"URL":"https:\/\/doi.org\/10.1145\/3678890.3678901","relation":{},"subject":[],"published":{"date-parts":[[2024,9,30]]},"assertion":[{"value":"2024-09-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}