漏洞|人工智能与信息安全：新的革命与挑战( 六 )

[6]Mnih, Volodymyr, et al. "Playing atari with deep reinforcement learning." arXiv preprint arXiv:1312.5602 (2013).
[7]Brown, Noam, et al. "Combining deep reinforcement learning and search for imperfect-information games." arXiv preprint arXiv:2007.13544 (2020).
[8]Brown, Tom B., et al. "Language models are few-shot learners." arXiv preprint arXiv:2005.14165 (2020).
[9]CLIP: Connecting Text and Images
[10]Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder. Ji Feng, Qi zhi Cai. Zhi Hua Zhou.
[11]McMahan, Brendan, et al. "Communication-efficient learning of deep networks from decentralized data." Artificial Intelligence and Statistics. PMLR, 2017.
[12]Yang, Qiang, et al. "Federated machine learning: Concept and applications." ACM Transactions on Intelligent Systems and Technology (TIST) 10.2 (2019): 1-19.
[13]Goodfellow, Ian J., Jonathon Shlens, and Christian Szegedy. "Explaining and harnessing adversarial examples." arXiv preprint arXiv:1412.6572 (2014).
[14]Madry, Aleksander, et al. "Towards deep learning models resistant to adversarial attacks." arXiv preprint arXiv:1706.06083 (2017).
[15]Carlini, Nicholas, and David Wagner. "Towards evaluating the robustness of neural networks." 2017 ieee symposium on security and privacy (sp). IEEE, 2017.
[16]Moosavi-Dezfooli, Seyed-Mohsen, Alhussein Fawzi, and Pascal Frossard. "Deepfool: a simple and accurate method to fool deep neural networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
[17]Ma, Chen, Li Chen, and Junhai Yong. "MetaSimulator: Simulating Unknown Target Models for Query-Efficient Black-box Attacks." arXiv preprint arXiv:2009.00960 (2020).
[18]Yin, Hongxu, et al. "See through Gradients: Image Batch Recovery via GradInversion." arXiv preprint arXiv:2104.07586 (2021).
[19]Generative Adversarial Networks. Ian J. Goodfellow, Jean Pouget-Abadie?, Mehdi Mirza, Bing Xu, David Warde. Sherjil Ozair, Aaron Courville, Yoshua Bengio.
[20]Weaponizing data science for social engineering: Automated E2E spear phishing on Twitter.
[21]Deep Learning for Cyber Vulnerability Discovery: NGTF Project Scoping Study. de Vel O, Hubczenko D, Kim J, Montague P, Xiang Y, Phung D, Zhang J, Murray T, Le T, Wen S, Liu S, Nguyen V, Lin G, Nguyen K, Le T, Nguyen T, Nock R, Qu L.
[22]Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection. Xiaojun Xu, Chang Liu, Qian Feng, Heng Yin, Le Song, Dawn Song.
[23]Neural Reverse Engineering of Stripped Binaries. Yaniv David. Uri Alon. Eran Yahav.
[24]WEDL-NIDS: improving network intrusion detection using word embedding-based deep learning method. Cui, J., Long, J., Min, E., Mao, Y.
[25]Autonomous Penetration Testing using Reinforcement Learning. Jonathon Schwartz.

[26]Network Traffic Anomaly Detection Using Shallow Packet Inspection and Parallel K-means Data Clustering. Radu Velea. Casian Ciobanu. Laurentiu Margarit. Ion Bica.