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Machine learning methods can detect Android malware with very high accuracy.
Dimensionality reduction by learning an invariant mapping
R. Hadsell, S. Chopra, and Y. LeCun · 2006
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A tutorial on conformal prediction
G. Shafer and V. Vovk · 2008
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Active learning literature survey
B. Settles · 2009
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Is self-supervised learning more robust than supervised learning?
Y. Zhong, H. Tang, J. Chen, J. Peng, and Y.-X. Wang · 2012
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Drebin: Effective and explainable detection of android malware in your pocket
D. Arp, M. Spreitzenbarth, M. Hubner, H. Gascon, K. Rieck, and C. Siemens · 2014
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Are your training datasets yet relevant? an investigation into the importance of timeline in machine learning-based malware detection
K. Allix, T. F. Bissyandé, J. Klein, and Y. Le Traon · 2015
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Unsupervised visual representation learning by context prediction
C. Doersch, A. Gupta, and A. A. Efros · 2015
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Facenet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
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AndroZoo: Collecting millions of android apps for the research community
K. Allix, T. F. Bissyandé, J. Klein, and Y. Le Traon · 2016
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Prescience: Probabilistic guidance on the retraining conundrum for malware detection
A. Deo, S. K. Dash, G. Suarez-Tangil, V. Vovk, and L. Cavallaro · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Y. Gal and Z. Ghahramani · 2016
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Reviewer integration and performance measurement for malware detection
B. Miller, A. Kantchelian, M. C. Tschantz, S. Afroz, R. Bachwani, R. Faizullabhoy, L. Huang, V. Shankar, T. Wu, G. Yiu, et al · 2016
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Adaptive and scalable android malware detection through online learning
A. Narayanan, L. Yang, L. Chen, and L. Jinliang · 2016
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On calibration of modern neural networks
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger · 2017
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Transcend: Detecting concept drift in malware classification models
R. Jordaney, K. Sharad, S. K. Dash, Z. Wang, D. Papini, I. Nouretdinov, and L. Cavallaro · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
B. Lakshminarayanan, A. Pritzel, and C. Blundell · 2017
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Context-aware, adaptive, and scalable android malware detection through online learning
A. Narayanan, M. Chandramohan, L. Chen, and Y. Liu · 2017
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Deep ground truth analysis of current android malware
F. Wei, Y. Li, S. Roy, X. Ou, and W. Zhou · 2017
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Hierarchical novelty detection for visual object recognition
K. Lee, K. Lee, K. Min, Y. Zhang, J. Shin, and H. Lee · 2018
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Poison frogs! targeted clean-label poisoning attacks on neural networks
A. Shafahi, W. R. Huang, M. Najibi, O. Suciu, C. Studer, T. Dumitras, and T. Goldstein · 2018
Cited alongside, same era.
MaMaDroid: Detecting android malware by building markov chains of behavioral models
L. Onwuzurike, E. Mariconti, P. Andriotis, E. De Cristofaro, G. Ross, and G. Stringhini · 2019
Cited alongside, same era.
TESSERACT: Eliminating experimental bias in malware classification across space and time
F. Pendlebury, F. Pierazzi, R. Jordaney, J. Kinder, L. Cavallaro, et al · 2019
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Droidevolver: Self-evolving android malware detection system
K. Xu, Y. Li, R. Deng, K. Chen, and J. Xu · 2019
Cited alongside, same era.
Learning loss for active learning
D. Yoo and I. S. Kweon · 2019
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Unsupervised learning of visual features by contrasting cluster assignments
Investigating labelless drift adaptation for malware detection
Z. Kan, F. Pendlebury, F. Pierazzi, and L. Cavallaro · 2021
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Best practices in pool-based active learning for image classification
A. Lang, C. Mayer, and R. Timofte · 2021
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Can we leverage predictive uncertainty to detect dataset shift and adversarial examples in android malware detection?
D. Li, T. Qiu, S. Chen, Q. Li, and S. Xu · 2021
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Prototypical contrastive learning of unsupervised representations
J. Li, P. Zhou, C. Xiong, and S. C. Hoi · 2021
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A survey of deep active learning
P. Ren, Y. Xiao, X. Chang, P.-Y. Huang, Z. Li, B. B. Gupta, X. Chen, and X. Wang · 2021
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Unsupervised feature learning by cross-level instance-group discrimination
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M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski, and A. Joulin · 2020
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A simple framework for contrastive learning of visual representations
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton · 2020
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Recent advances in open set recognition: A survey
C. Geng, S.-j. Huang, and S. Chen · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick · 2020
Cited alongside, same era.
Supervised contrastive learning
P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan · 2020
Cited alongside, same era.
Energy-based out-of-distribution detection
W. Liu, X. Wang, J. Owens, and Y. Li · 2020
Cited alongside, same era.
Calibrating deep neural networks using focal loss
J. Mukhoti, V. Kulharia, A. Sanyal, S. Golodetz, P. Torr, and P. Dokania · 2020
Cited alongside, same era.
X. Wang, Z. Liu, and S. X. Yu · 2021
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BODMAS: An open dataset for learning based temporal analysis of PE malware
L. Yang, A. Ciptadi, I. Laziuk, A. Ahmadzadeh, and G. Wang · 2021
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CADE: Detecting and explaining concept drift samples for security applications
L. Yang, W. Guo, Q. Hao, A. Ciptadi, A. Ahmadzadeh, X. Xing, and G. Wang · 2021
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Dos and don’ts of machine learning in computer security
D. Arp, E. Quiring, F. Pendlebury, A. Warnecke, F. Pierazzi, C. Wressnegger, L. Cavallaro, and K. Rieck · 2022
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Transcending transcend: Revisiting malware classification in the presence of concept drift
F. Barbero, F. Pendlebury, F. Pierazzi, and L. Cavallaro · 2022
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HCSC: hierarchical contrastive selective coding
Y. Guo, M. Xu, J. Li, B. Ni, X. Zhu, Z. Sun, and Y. Xu · 2022
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A neural pre-conditioning active learning algorithm to reduce label complexity
S. T. Kong, S. Jeon, D. Na, J. Lee, H.-S. Lee, and K.-H. Jung · 2022
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A survey on active deep learning: From model driven to data driven
P. Liu, L. Wang, R. Ranjan, G. He, and L. Zhao · 2022
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Meta-query-net: Resolving purity-informativeness dilemma in open-set active learning
D. Park, Y. Shin, J. Bang, Y. Lee, H. Song, and J.-G. Lee · 2022
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On the limitations of continual learning for malware classification
M. S. Rahman, S. Coull, and M. Wright · 2022
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Out-of-distribution detection with deep nearest neighbors
Y. Sun, Y. Ming, X. Zhu, and Y. Li · 2022
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A comparative survey of deep active learning
X. Zhan, Q. Wang, K.-h. Huang, H. Xiong, D. Dou, and A. B. Chan · 2022
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Is it overkill? analyzing feature-space concept drift in malware detectors
Z. Chen, Z. Zhang, Z. Kan, L. Yang, J. Cortellazzi, F. Pendlebury, F. Pierazzi, L. Cavallaro, and G. Wang · 2023
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