Titlebook: Artificial Intelligence for Cybersecurity; Mark Stamp,Corrado Aaron Visaggio,Fabio Di Troia Book 2022 The Editor(s) (if applicable) and Th

本能

Mark Stamp,Corrado Aaron Visaggio,Fabio Di TroiaPresents new and novel applications for AI technology within the context of cybersecurity.Explores and conquers issues and obstacles that the AI field is tackling within the context of cybersecurity.T

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Advances in Information Securityhttp://image.papertrans.cn/b/image/162364.jpg

chandel

https://doi.org/10.1007/978-1-349-15821-8samples. While the AC-GAN generated images often appear to be very similar to real malware images, we conclude that from a deep learning perspective, the AC-GAN generated samples do not rise to the level of deep fake malware images.

小臼

https://doi.org/10.1007/978-3-031-40419-1ation algorithms, we used and compared Support Vector Machines (SVM), Logistic Regression, Random Forests, and Multi-Layer Perceptron (MLP). We found that the classification accuracy obtained by the word embeddings generated by BERT is effective in detecting malware samples, and superior in accuracy when compared to the ones created by Word2Vec.

滔滔不絕的人

Ancillary

BERT for Malware Classificationation algorithms, we used and compared Support Vector Machines (SVM), Logistic Regression, Random Forests, and Multi-Layer Perceptron (MLP). We found that the classification accuracy obtained by the word embeddings generated by BERT is effective in detecting malware samples, and superior in accuracy when compared to the ones created by Word2Vec.

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搖擺

Assessing the Robustness of an Image-Based Malware Classifier with Smali Level Perturbations Techniqtector and evaluate its resilience when morphed samples are considered. The experiments were conducted on 16384 real-world Android Malware, and the experimental analysis demonstrates that standard image-based malware classifiers are vulnerable to simple perturbations attacks.

含水層

ostracize