The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Large-scale malware classification using random projections and neural networks
Abstract
Automatically generated malware is a significant problem for computer users. Analysts are able to manually investigate a small number of unknown files, but the best large-scale defense for detecting malware is automated malware classification. Malware classifiers often use sparse binary features, and the number of potential features can be on the order of tens or hundreds of millions. Feature selection reduces the number of features to a manageable number for training simpler algorithms such as logistic regression, but this number is still too large for more complex algorithms such as neural networks. To overcome this problem, we used random projections to further reduce the dimensionality of the original input space. Using this architecture, we train several very large-scale neural network systems with over 2.6 million labeled samples thereby achieving classification results with a two-class error rate of 0.49% for a single neural network and 0.42% for an ensemble of neural networks.
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Cites
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Cited by
Towards Evaluating the Robustness of Neural Networks
Carlini, N., & Wagner, D. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.49 · Cited by 8,859
The Limitations of Deep Learning in Adversarial Settings
Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z. B., & Swami, A. (2016) · 2016 IEEE European Symposium on Security and Privacy (EuroS&P) · doi:10.1109/eurosp.2016.36 · Cited by 3,993
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
Xu, W., Evans, D., & Qi, Y. (2018) · Proceedings 2018 Network and Distributed System Security Symposium · doi:10.14722/ndss.2018.23198 · Cited by 1,768
Adversarial Examples: Attacks and Defenses for Deep Learning
Yuan, X., He, P., Zhu, Q., & Li, X. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2886017 · Cited by 1,733
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
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Metadata
- Authors
- Dahl, G. E., Stokes, J. W., Deng, L., & Yu, D.
- Venue
- 2013 IEEE International Conference on Acoustics, Speech and Signal Processing
- Publisher
- IEEE
- Published
- 2013-05
- Pages
- 3422-3426
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W1966948031
- Cited by
- 439
- References
- 5
- FWCI
- 12.79
- Type
- conference paper
- Language
- en
- Open access
- closed
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