A Modified Maximal Divergence Sequential Auto-Encoder and Time Delay Neural Network Models for Vulnerable Binary Codes Detection

Since the risks associated with software vulnerabilities are rapidly increasing, the detection of vulnerabilities in binary code has become an important area of concern for the software community. However, research studies associated with the detection of vulnerabilities in binary code remain limite...

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Veröffentlicht in:IEEE access Jg. 8; S. 14999 - 15006
1. Verfasser: Albahar, Marwan Ali
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Binary code vulnerability detection
Binary codes
Coders
Codes
deep learning
Feature extraction
NDSS18 binary dataset
Neural networks
new regularization technique
Performance evaluation
Performance prediction
Regularization
Security
Software
time delay neural network
Time lag
Training
ISSN:2169-3536, 2169-3536
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Zusammenfassung:Since the risks associated with software vulnerabilities are rapidly increasing, the detection of vulnerabilities in binary code has become an important area of concern for the software community. However, research studies associated with the detection of vulnerabilities in binary code remain limited to the handcrafted features referenced by a specific group of experts in the field. This paper considers other possibilities to add on the subject of detecting vulnerabilities in binary code. Herein, we utilize recent studies conducted on the topic of deep learning and specifically study a maximal divergence sequential auto-encoder (MDSAE) model to propose a modified version (MDSAE-NR). We also propose an altered interpretation of time-delay neural network (TDNN-NR) by incorporating a new regularization technique that produced optimized results. Finally, both models achieved good predictive performance using different evaluation metrics such as accuracy, recall, precision and F1 score compared to the baseline results. Based on the results of our experiments, we observed a 2 to 2.5% average improvement in each performance measure of interest.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2965726