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Despite many attempts, the state-of-the-art of adversarial machine learning on malware detection systems generally yield unexecutable samples.
G. Conti, S. Bratus, A. Shubina, A. Lichtenberg, R. Ragsdale, R. Perez-Alemany, B. Sangster, and M. Supan, “A visual study of primitive binary fragment types,” White Paper, Black Hat USA , 2010
2010
Earlier work this paper cites.
S. K. Cha, B. Pak, D. Brumley, and R. J. Lipton, “Platform-independent programs,” in Proceedings of the 17th ACM conference on Computer and communications security , 2010, pp. 547–558
2010
Earlier work this paper cites.
L. Nataraj, S. Karthikeyan, G. Jacob, and B. Manjunath, “Malware images: visualization and automatic classification,” in Proceedings of the 8th international symposium on visualization for cyber security , 2011, p. 4
2011
Earlier work this paper cites.
D. Lee, I. S. Song, K. J. Kim, and J.-h. Jeong, “A study on malicious codes pattern analysis using visualization,” in Proceedings of the International Conference on Information Science and Applications (ICISA) , 2011, pp. 1–5
2011
Earlier work this paper cites.
L. Nataraj, S. Karthikeyan, G. Jacob, and B. Manjunath, “Malware images: visualization and automatic classification,” in Proceedings of the 8th international symposium on visualization for cyber security , 2011, p. 4
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proceedings of Advances in neural information processing systems , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
D. C. Ciresan, U. Meier, and J. Schmidhuber, “Multi-column deep neural networks for image classification,” in IEEE Conference on Computer Vision and Pattern Recognition, Providence , 2012, pp. 3642–3649
2012
Earlier work this paper cites.
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” in Proceedings of the 2014 International Conference on Learning Representations. , 2014
2014
Earlier work this paper cites.
D. Arp, M. Spreitzenbarth, M. Hubner, H. Gascon, and K. Rieck, “DREBIN: effective and explainable detection of android malware in your pocket,” in Proceedings of the 21st Annual Network and Distributed System Security Symposium, NDSS , 2014
2014
Earlier work this paper cites.
P. Faruki, V. Laxmi, A. Bharmal, M. S. Gaur, and V. Ganmoor, “Androsimilar: Robust signature for detecting variants of android malware,” Journal of Information Security and Applications , vol. 22, pp. 66–80, 2015
2015
Earlier work this paper cites.
H. Kang, J.-w. Jang, A. Mohaisen, and H. K. Kim, “Detecting and classifying android malware using static analysis along with creator information,” International Journal of Distributed Sensor Networks , vol. 11, no. 6, p. 479174, 2015
2015
Earlier work this paper cites.
K. Han, J. H. Lim, B. Kang, and E. G. Im, “Malware analysis using visualized images and entropy graphs,” International Journal of Information Security , vol. 14, no. 1, pp. 1–14, 2015
2015
Earlier work this paper cites.
B. Alipanahi, A. Delong, M. T. Weirauch, and B. J. Frey, “Predicting the sequence specificities of dna-and rna-binding proteins by deep learning,” Nature biotechnology , vol. 33, no. 8, p. 831, 2015
2015
Earlier work this paper cites.
A. Mohaisen, O. Alrawi, and M. Mohaisen, “AMAL: high-fidelity, behavior-based automated malware analysis and classification,” Computers & Security , vol. 52, pp. 251–266, 2015
2015
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” in Proceedings of the 2015 International Conference on Learning Representations. , 2015
2015
Earlier work this paper cites.
Y. M. P. Pa, S. Suzuki, K. Yoshioka, T. Matsumoto, T. Kasama, and C. Rossow, “Iotpot: analysing the rise of iot compromises,” EMU , vol. 9, p. 1, 2015
2015
Earlier work this paper cites.
S. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, “Deepfool: A simple and accurate method to fool deep neural networks,” in Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR , 2016, pp. 2574–2582
2016
Earlier work this paper cites.
N. Papernot, P. D. McDaniel, X. Wu, S. Jha, and A. Swami, “Distillation as a defense to adversarial perturbations against deep neural networks,” in Proceedings of the IEEE Symposium on Security and Privacy, SP Workshops , 2016, pp. 582–597
2016
Earlier work this paper cites.
J. Zhang, Z. Qin, H. Yin, L. Ou, and Y. Hu, “IRMD: malware variant detection using opcode image recognition,” in Proceedings of the 22nd IEEE International Conference on Parallel and Distributed Systems, ICPADS , 2016, pp. 1175–1180
2016
Earlier work this paper cites.
N. Papernot, P. D. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, “The limitations of deep learning in adversarial settings,” in IEEE European Symposium on Security and Privacy, EuroS&P , 2016, pp. 372–387
2016
Cited alongside, same era.
T. Miyato, S.-i. Maeda, M. Koyama, K. Nakae, and S. Ishii, “Distributional smoothing with virtual adversarial training,” in Proceedings of the 2016 International Conference on Learning Representations. , 2016
2016
Cited alongside, same era.
Y. M. P. Pa, S. Suzuki, K. Yoshioka, T. Matsumoto, T. Kasama, and C. Rossow, “Iotpot: A novel honeypot for revealing current iot threats,” JIP , vol. 24, no. 3, pp. 522–533, 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
J. Su, D. V. Vargas, S. Prasad, D. Sgandurra, Y. Feng, and K. Sakurai, “Lightweight classification of iot malware based on image recognition,” in Proceedings of the 2018 IEEE Annual Computer Software and Applications Conference, COMPSAC , 2018, pp. 664–669
2018
Later among the works it cites.
Z. Cui, F. Xue, X. Cai, Y. Cao, G. Wang, and J. Chen, “Detection of malicious code variants based on deep learning,” IEEE Trans. Industrial Informatics , vol. 14, no. 7, pp. 3187–3196, 2018
2018
Later among the works it cites.
J. Fu, J. Xue, Y. Wang, Z. Liu, and C. Shan, “Malware visualization for fine-grained classification,” IEEE Access , vol. 6, pp. 14 510–14 523, 2018
2018
Later among the works it cites.
B. Kolosnjaji, A. Demontis, B. Biggio, D. Maiorca, G. Giacinto, C. Eckert, and F. Roli, “Adversarial malware binaries: Evading deep learning for malware detection in executables,” in Proceedings of the 26th European Signal Processing Conference, EUSIPCO , 2018, pp. 533–537
2018
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A. Bulazel and B. Yener, “A survey on automated dynamic malware analysis evasion and counter-evasion: Pc, mobile, and web,” in Proceedings of the 1st Reversing and Offensive-oriented Trends Symposium . ACM, 2017, p. 2
2017
Cited alongside, same era.
R. R. Gómez, Y. Nadji, and M. Antonakakis, “Towards designing effective visualizations for dns-based network threat analysis,” in 2017 IEEE Symposium on Visualization for Cyber Security, VizSec 2017, Phoenix, AZ, USA, October 2, 2017 , 2017, pp. 1–8
2017
Cited alongside, same era.
A. Makandar and A. Patrot, “Malware class recognition using image processing techniques,” in Proceedings of the 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI) , 2017, pp. 76–80
2017
Cited alongside, same era.
K. Grosse, N. Papernot, P. Manoharan, M. Backes, and P. D. McDaniel, “Adversarial examples for malware detection,” in Proceedings of the 22nd European Symposium on Research Computer Security - ESORICS, Part II , 2017, pp. 62–79
2017
Cited alongside, same era.
“Deep learning and machine learning differences: Recent views in an ongoing debate,” Mar 2017. [Online]. Available: http://www.dataversity.net/deep-learning-machine-learning-differences-recent-views-ongoing-debate/
2017
Cited alongside, same era.
N. Carlini and D. A. Wagner, “Towards evaluating the robustness of neural networks,” in Proceedings of the 2017 IEEE Symposium on Security and Privacy, SP 2017 , 2017, pp. 39–57
2017
Cited alongside, same era.
N. Papernot, P. D. McDaniel, I. J. Goodfellow, S. Jha, Z. B. Celik, and A. Swami, “Practical black-box attacks against machine learning,” in Proceedings of the ACM on Asia Conference on Computer and Communications Security, AsiaCCS , 2017, pp. 506–519
2017
Cited alongside, same era.
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami, “Practical black-box attacks against machine learning,” in Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security , 2017, pp. 506–519
2017
Cited alongside, same era.
Later among the works it cites.
F. Kreuk, A. Barak, S. Aviv-Reuven, M. Baruch, B. Pinkas, and J. Keshet, “Deceiving end-to-end deep learning malware detectors using adversarial examples,” in Proceedings of the NeurIPS Workshop on Security in Machine Learning , 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
B. Wang, Y. Yao, B. Viswanath, H. Zheng, and B. Y. Zhao, “With great training comes great vulnerability: practical attacks against transfer learning,” in Proceedings of the 27th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 18) , 2018, pp. 1281–1297
2018
Later among the works it cites.
2018
Later among the works it cites.
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” in International Conference on Learning Representations , 2018
2018
Later among the works it cites.
F. Tramèr, A. Kurakin, N. Papernot, D. Boneh, and P. D. McDaniel, “Ensemble adversarial training: Attacks and defenses,” in Proceedings of the 2018 International Conference on Learning Representations. , 2018
2018
Later among the works it cites.
Y. Dong, F. Liao, T. Pang, H. Su, J. Zhu, X. Hu, and J. Li, “Boosting adversarial attacks with momentum,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018
2018
Later among the works it cites.
S. Ni, Q. Qian, and R. Zhang, “Malware identification using visualization images and deep learning,” Computers & Security , vol. 77, pp. 871–885, 2018
2018
Later among the works it cites.
Y. Fan, S. Hou, Y. Zhang, Y. Ye, and M. Abdulhayoglu, “Gotcha - sly malware!: Scorpion A metagraph2vec based malware detection system,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19-23, 2018 , 2018, pp. 253–262
2018
Later among the works it cites.
C. D. McDermott, F. Majdani, and A. Petrovski, “Botnet detection in the internet of things using deep learning approaches.” in Proceedings of the International joint conference on neural networks 2018 (IJCNN 2018) . ACM, 2018
2018
Later among the works it cites.
H. HaddadPajouh, A. Dehghantanha, R. Khayami, and K.-K. R. Choo, “A deep recurrent neural network based approach for internet of things malware threat hunting,” Future Generation Computer Systems , vol. 85, pp. 88–96, 2018
2018
Later among the works it cites.
A. Abusnaina, A. Khormali, H. Alasmary, J. Park, A. Anwar, and A. Mohaisen, “Adversarial learning attacks on graph-based IoT malware detection systems,” in Proceedings of the 39th IEEE International Conference on Distributed Computing Systems, ICDCS , 2019
2019
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A. Khormali, A. Abusnaina, D. Nyang, M. Yuksel, and A. Mohaisen, “Examining the robustness of learning-based ddos detection in software defined networks,” in Proceedings of the IEEE conference on dependable and secure computing, IDSC , 2019
2019
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X. Liu, J. Zhang, Y. Lin, and H. Li, “Atmpa: Attacking machine learning-based malware visualization detection methods via adversarial examples,” 2019
2019
Closest in time.