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Deep neural network based malware detection using two dimensional binary program features
Abstract
In this paper we introduce a deep neural network based malware detection system that Invincea has developed, which achieves a usable detection rate at an extremely low false positive rate and scales to real world training example volumes on commodity hardware. We show that our system achieves a 95% detection rate at 0.1% false positive rate (FPR), based on more than 400,000 software binaries sourced directly from our customers and internal malware databases. In addition, we describe a non-parametric method for adjusting the classifier's scores to better represent expected precision in the deployment environment. Our results demonstrate that it is now feasible to quickly train and deploy a low resource, highly accurate machine learning classification model, with false positive rates that approach traditional labor intensive expert rule based malware detection, while also detecting previously unseen malware missed by these traditional approaches. Since machine learning models tend to improve with larger datasizes, we foresee deep neural network classification models gaining in importance as part of a layered network defense strategy in coming years.
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Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
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Gupta, M., Abdelsalam, M., Khorsandroo, S., & Mittal, S. (2020) · IEEE Access · doi:10.1109/access.2020.2975142 · Cited by 609
A Survey of Deep Learning Methods for Cyber Security
Berman, D. S., Buczak, A. L., Chavis, J. S., & Corbett, C. L. (2019) · Information · doi:10.3390/info10040122 · Cited by 569
A Multimodal Deep Learning Method for Android Malware Detection Using Various Features
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Metadata
- Authors
- Saxe, J., & Berlin, K.
- Venue
- 2015 10th International Conference on Malicious and Unwanted Software (MALWARE)
- Publisher
- IEEE
- Published
- 2015-10
- Pages
- 11-20
- OpenAlex
- W1893133781
- Cited by
- 711
- References
- 8
- FWCI
- 23.58
- Type
- conference paper
- Language
- en
- Open access
- closed
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