Windows Malware Hunting with InceptionResNetv2 Assisted Malware Visualization Approach
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-0609-3_12
Reference38 articles.
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3. Amer E, Zelinka I (2020) A dynamic Windows malware detection and prediction method based on contextual understanding of API call sequence. Comput Secur 92:101760. https://doi.org/10.1016/j.cose.2020.101760
4. Peng X, Xian H, Lu Q, Lu X (2021) Semantics aware adversarial malware examples generation for black-box attacks. Appl Soft Comput 109:107506. https://doi.org/10.1016/j.asoc.2021.107506
5. Vasan D, Alazab M, Wassan S, Naeem H, Safaei B, Zheng Q (2020) IMCFN: Image-based malware classification using fine-tuned convolutional neural network architecture. Comput Netw 171:107138. https://doi.org/10.1016/j.comnet.2020.107138
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