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The Microsoft Malware Classification Challenge was announced in 2015 along with a publication of a huge dataset of nearly 0.5 terabytes, consisting of disassembly and bytecode of more than 20K malware samples.
Technology in a Changing World
G.S. Shahi, E.F. Pang, and P.P.E. Fong · 2009
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Rebus : un bus de communication facilitant la coopération entre outils d’analyse de sécurité
Philippe Biondi, Xavier Mehrenberger, and Sarah Zennou · 2015
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A HYBRID INTELLIGENCE/MULTI-AGENT SYSTEM FOR MINING INFORMATION ASSURANCE DATA
Charles A. Fowler · 2015
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Deep neural network based malware detection using two dimensional binary program features
J. Saxe and K. Berlin · 2015
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Novel feature extraction, selection and fusion for effective malware family classification
Mansour Ahmadi, Dmitry Ulyanov, Stanislav Semenov, Mikhail Trofimov, and Giorgio Giacinto · 2016
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On the feasibility of malware authorship attribution
Saed Alrabaee, Paria Shirani, Mourad Debbabi, and Lingyu Wang · 2016
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Itect: Scalable information theoretic similarity for malware detection
Sukriti Bhattacharya, Héctor D. Menéndez, Earl T. Barr, and David Clark · 2016
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On normalized compression distance and large malware
Rebecca Schuller Borbely · 2016
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One-class svm with privileged information and its application to malware detection
E. Burnaev and D. Smolyakov · 2016
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One-class machine of reference vectors using privileged information
Evgeny Burnayev and Dmitry Smolyakov · 2016
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Building better detection with privileged information
Z. Berkay Celik, Patrick D. McDaniel, Rauf Izmailov, Nicolas Papernot, and Ananthram Swami · 2016
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Malware sequence alignment
A. Dinh, D. Brill, Y. Li, and W. He · 2016
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Polymorphic malware detection using sequence classification methods
J. Drew, T. Moore, and M. Hahsler · 2016
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Classifying network attack data using random forest
Tonya Fields and Jonathan Graham · 2016
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Random forest for malware classification
Felan Carlo C. Garcia and Felix P. Muga II · 2016
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Scalable malware classification with multifaceted content features and threat intelligence
X. Hu, J. Jang, T. Wang, Z. Ashraf, M. P. Stoecklin, and D. Kirat · 2016
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Evlauation of Hidden Markov Model for Malware Behavioural Classification
Mohammad Imran · 2016
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Performance analysis of the malware classification method in accordance with the changes in assembly code
Nak-Hyun Kim, Byung ik Kim, and Tae jin Lee · 2016
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Performance analysis of machine learning and pattern recognition algorithms for malware classification
B. N. Narayanan, O. Djaneye-Boundjou, and T. M. Kebede · 2016
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Projecting “better than randomly”: How to reduce the dimensionality of very large datasets in a way that outperforms random projections
M. Wojnowicz, D. Zhang, G. Chisholm, X. Zhao, and M. Wolff · 2016
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Using multi-features and ensemble learning method for imbalanced malware classification
Y. Zhang, Q. Huang, X. Ma, Z. Yang, and J. Jiang · 2016
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Measuring the effectiveness of generic malware models, 2017
Naman Bagga · 2017
Cited alongside, same era.
Quincy: Detecting host-based code injection attacks in memory dumps
Thomas Barabosch, Niklas Bergmann, Adrian Dombeck, and Elmar Padilla · 2017
Cited alongside, same era.
Feature cultivation in privileged information-augmented detection
Z. Berkay Celik, Patrick McDaniel, and Rauf Izmailov · 2017
Cited alongside, same era.
Polymorphic malware detection using sequence classification methods and ensembles
Lempel-ziv jaccard distance, an effective alternative to ssdeep and sdhash
Edward Raff and Charles K. Nicholas · 2017
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Deep learning for network flow analysis and malware classification
R. K. Rahul, T. Anjali, Vijay Krishna Menon, and K. P. Soman · 2017
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Fast model learning for the detection of malicious digital documents
Daniel Scofield, Craig Miles, and Stephen Kuhn · 2017
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Webeye automated collection of malicious http traffic
Johann Vierthaler, Roman Kruszelnicki, and Julian Schütte · 2017
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A theoretical framework for robustness of (deep) classifiers under adversarial noise
Beilun Wang, Ji Gao, and Yanjun Qi · 2017
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Jake Drew, Michael Hahsler, and Tyler Moore · 2017
Cited alongside, same era.
Spabox: Safeguarding privacy during deep packet inspection at a middlebox
J. Fan, C. Guan, K. Ren, Y. Cui, and C. Qiao · 2017
Cited alongside, same era.
Deepcloak: Masking deep neural network models for robustness against adversarial samples
Ji Gao, Beilun Wang, Zeming Lin, and Weilin Xu Yanjun Qi · 2017
Cited alongside, same era.
Efficient sequence regression by learning linear models in all-subsequence space
Severin Gsponer, Barry Smyth, and Georgiana Ifrim · 2017
Cited alongside, same era.
The study of keyword search in open source search engines and digital forensics tools with respect to the needs of cyber crime investigations, 2017
Joachim Hansen · 2017
Cited alongside, same era.
Malware classification using static analysis based features
M. Hassen, M. Carvalho, and P. Chan · 2017
Cited alongside, same era.
Scalable function call graph-based malware classification
Mehadi Hassen and Philip K. Chan · 2017
Cited alongside, same era.
Autoencoder-based feature learning for cyber security applications
M. Yousefi-Azar, V. Varadharajan, L. Hamey, and U. Tupakula · 2017
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Imbalanced malware images classification: a CNN based approach
Songqing Yue · 2017
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Malware detection based on deep learning algorithm
Ding Yuxin and Zhu Siyi · 2017
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Based on multi-features and clustering ensemble method for automatic malware categorization
Y. Zhang, C. Rong, Q. Huang, Y. Wu, Z. Yang, and J. Jiang · 2017
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Multinomial malware classification based on call graphs, May 2017
Morten Oscar Østbye · 2017
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Learning a Neural-network-based Representation for Open Set Recognition
M. Hassen and P. K. Chan · 2018
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Feature selection for malware classification
Seong Oun Hwanga, Trong Kha Nguyenb, and Vu Duc Ly · 2018
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Image-based malware classification using convolutional neural network
Hae-Jung Kim · 2018
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Adversarial Examples on Discrete Sequences for Beating Whole-Binary Malware Detection
F. Kreuk, A. Barak, S. Aviv-Reuven, M. Baruch, B. Pinkas, and J. Keshet · 2018
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Performance Comparison of Machine Learning Algorithms for Malware Detection
Hyun-Jong Lee, Heo-Jae Hyeok, and Doosung Hwang · 2018
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Sam cybersecurity engagement kit, 2018
Microsoft · 2018
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Feature squeezing: Detecting adversarial examples in deep neural networks
Weilin Xu, David Evans, and Yanjun Qi · 2018
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Effective malware detection based on behaviour and data features
Zhiwu Xu, Cheng Wen, Shengchao Qin, and Zhong Ming · 2018
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Detecting malware with an ensemble method based on deep neural network
Jinpei Yan, Yong Qi, and Qifan Rao · 2018
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