Abstract
With the increase of malware, traditional malicious detection methods are not suitable to high-intensity detection work. In response to this question, many malware detection methods based on Machine Learning (ML-based) are proposed to address this problem. However, the ML-based detection method is vulnerable to the attack from adversarial samples. To overcome the limitation, we present our model. We protect the classification model from adversarial example attack by quantifying the similarity between the extracted image features and the expected features of the prediction class. Experimental results demonstrate that our model can detect the misclassification caused by adversarial samples with a higher accuracy than that of the Resnet-50.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China under Grants No. 61572170 and 61672206, Program for Hundreds of Outstanding Innovative Talents in Higher Education Institutions of Hebei Province (III) under Grant No. SLRC2017042, and Natural Science Foundation of Hebei Province of China under Grant No. F2019205163, and department of Human Resoueces and Social Security of Hebei Province under Grant No. 201901028.
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Wang, F., Lu, Y., Li, Q., Wang, C. (2020). A Feature-Based Detection System of Adversarial Sample Attack. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12487. Springer, Cham. https://doi.org/10.1007/978-3-030-62460-6_44
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DOI: https://doi.org/10.1007/978-3-030-62460-6_44
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