Securing Machine Learning in the Cloud: A Systematic Review of Cloud Machine Learning Security
暂无分享,去创建一个
Junaid Qadir | Ala Al-Fuqaha | Yehia Elkhatib | Aneeqa Ijaz | Waleed Iqbal | Muhammad Usama | Adnan Qayyum | Waleed Iqbal | A. Qayyum | Junaid Qadir | Ala Al-Fuqaha | Y. Elkhatib | M. Usama | Aneeqa Ijaz
[1] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[2] Sergey Ioffe,et al. Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Mahesh K. Marina,et al. Examining Machine Learning for 5G and Beyond Through an Adversarial Lens , 2020, IEEE Internet Computing.
[4] Wei You,et al. Cracking Classifiers for Evasion: A Case Study on the Google's Phishing Pages Filter , 2016, WWW.
[5] Mario Fritz,et al. ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models , 2018, NDSS.
[6] Dawn Xiaodong Song,et al. Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning , 2017, ArXiv.
[7] Luigi V. Mancini,et al. Evasion Attacks Against Watermarking Techniques found in MLaaS Systems , 2019, 2019 Sixth International Conference on Software Defined Systems (SDS).
[8] Alberto Ferreira de Souza,et al. Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data , 2018, 2018 International Joint Conference on Neural Networks (IJCNN).
[9] Xiaoqian Jiang,et al. SecureLR: Secure Logistic Regression Model via a Hybrid Cryptographic Protocol , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[10] Xiaogang Wang,et al. Deep Learning Face Representation from Predicting 10,000 Classes , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[11] Ting Wang,et al. Model-Reuse Attacks on Deep Learning Systems , 2018, CCS.
[12] Zhenkai Liang,et al. Neural Network Inversion in Adversarial Setting via Background Knowledge Alignment , 2019, CCS.
[13] Andrew Zisserman,et al. Deep Face Recognition , 2015, BMVC.
[14] Fan Zhang,et al. Stealing Machine Learning Models via Prediction APIs , 2016, USENIX Security Symposium.
[15] Daniel Bernau,et al. Monte Carlo and Reconstruction Membership Inference Attacks against Generative Models , 2019, Proc. Priv. Enhancing Technol..
[16] Pan He,et al. Adversarial Examples: Attacks and Defenses for Deep Learning , 2017, IEEE Transactions on Neural Networks and Learning Systems.
[17] Robert Nikolai Reith,et al. Efficiently Stealing your Machine Learning Models , 2019, WPES@CCS.
[18] Dinh Thai Hoang,et al. Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning , 2020, ArXiv.
[19] Clément Farabet,et al. Torch7: A Matlab-like Environment for Machine Learning , 2011, NIPS 2011.
[20] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[21] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[22] Siddharth Garg,et al. BadNets: Evaluating Backdooring Attacks on Deep Neural Networks , 2019, IEEE Access.
[23] Tam N. Nguyen,et al. Attacking Machine Learning models as part of a cyber kill chain , 2017, ArXiv.
[24] Matthias Bethge,et al. Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models , 2017, ICLR.
[25] Sagar Sharma,et al. Image Disguising for Privacy-preserving Deep Learning , 2018, CCS.
[26] Dongqi Han,et al. Practical Traffic-space Adversarial Attacks on Learning-based NIDSs , 2020, ArXiv.
[27] Yang Liu,et al. Advanced evasion attacks and mitigations on practical ML‐based phishing website classifiers , 2020, Int. J. Intell. Syst..
[28] Yanjiao Chen,et al. Backdoor Attacks and Defenses for Deep Neural Networks in Outsourced Cloud Environments , 2020, IEEE Network.
[29] Sencun Zhu,et al. Server-Based Manipulation Attacks Against Machine Learning Models , 2018, CODASPY.
[30] Bo Li,et al. Automated poisoning attacks and defenses in malware detection systems: An adversarial machine learning approach , 2017, Comput. Secur..
[31] Radha Poovendran,et al. Attacking Automatic Video Analysis Algorithms: A Case Study of Google Cloud Video Intelligence API , 2017, MPS@CCS.
[32] Junaid Qadir,et al. The Adversarial Machine Learning Conundrum: Can the Insecurity of ML Become the Achilles' Heel of Cognitive Networks? , 2019, IEEE Network.
[33] Mehmed M. Kantardzic,et al. Data Driven Exploratory Attacks on Black Box Classifiers in Adversarial Domains , 2017, Neurocomputing.
[34] Michele Colajanni,et al. Addressing Adversarial Attacks Against Security Systems Based on Machine Learning , 2019, 2019 11th International Conference on Cyber Conflict (CyCon).
[35] Tian Liu,et al. FDA$^3$: Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications , 2020, IEEE Transactions on Industrial Informatics.
[36] Vijay Arya,et al. Model Extraction Warning in MLaaS Paradigm , 2017, ACSAC.
[37] Philip S. Yu,et al. Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud , 2018, KDD.
[38] Junaid Qadir,et al. Black-box Adversarial Machine Learning Attack on Network Traffic Classification , 2019, 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC).
[39] Farinaz Koushanfar,et al. ReDCrypt: Real-Time Privacy-Preserving Deep Learning Inference in Clouds Using FPGAs , 2018, ACM Trans. Reconfigurable Technol. Syst..
[40] Gabriele Costa,et al. WAF-A-MoLE: evading web application firewalls through adversarial machine learning , 2020, SAC.
[41] Junaid Qadir,et al. Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and the Way Forward , 2019, IEEE Communications Surveys & Tutorials.
[42] Nikhil Joshi,et al. GDALR: An Efficient Model Duplication Attack on Black Box Machine Learning Models , 2019, 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN).
[43] Vitaly Shmatikov,et al. Membership Inference Attacks Against Machine Learning Models , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[44] Tao Liu,et al. SIN2: Stealth infection on neural network — A low-cost agile neural Trojan attack methodology , 2018, 2018 IEEE International Symposium on Hardware Oriented Security and Trust (HOST).
[45] Magnus Nyström,et al. Adversarial Machine Learning - Industry Perspectives , 2020, SSRN Electronic Journal.
[46] Ajmal Mian,et al. Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey , 2018, IEEE Access.
[47] Ben Y. Zhao,et al. With Great Training Comes Great Vulnerability: Practical Attacks against Transfer Learning , 2018, USENIX Security Symposium.
[48] Junaid Qadir,et al. Secure and Robust Machine Learning for Healthcare: A Survey , 2020, IEEE Reviews in Biomedical Engineering.
[49] Jiajie Zhang,et al. Defending Adversarial Attacks on Cloud-aided Automatic Speech Recognition Systems , 2019, SCC '19.
[50] Christian Poellabauer,et al. Real-Time Adversarial Attacks , 2019, IJCAI.
[51] Prateek Mittal,et al. Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples , 2019, ArXiv.
[52] Tom Goldstein,et al. Adversarial attacks on Copyright Detection Systems , 2019, ICML.
[53] Hassan Takabi,et al. Privacy-preserving Machine Learning in Cloud , 2017, CCSW.
[54] Yao Lu,et al. Oblivious Neural Network Predictions via MiniONN Transformations , 2017, IACR Cryptol. ePrint Arch..