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Keyword

machine learning security

Description: The study of threats, vulnerabilities, defenses, and security applications involving machine-learning systems and their data.
OpenAlex ID: keywords/machine-learning-security
Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks
2016 · Nicolas Papernot, Patrick McDaniel, et al. · Proceedings - IEEE Symposium on Security and Privacy/Proceedings of the ... IEEE Symposium on Security and Privacy · 3,114
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Adversarial Examples Are Not Easily Detected
2017 · Nicholas Carlini, David Wagner · ACM Workshop on Artificial Intelligence and Security (AISec) · 1,344
Can machine learning be secure?
2006 · Marco Barreno, Blaine Nelson, et al. · ACM Symposium on Information, Computer and Communications Security · 849
Adversarial Attacks and Defenses in Deep Learning
2020 · Kui Ren, Tianhang Zheng, et al. · Engineering · 677
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SoK: Security and Privacy in Machine Learning
2018 · Nicolas Papernot, Patrick McDaniel, et al. · IEEE European Symposium on Security and Privacy · 501
On Adaptive Attacks to Adversarial Example Defenses
2020 · Tramer, Florian, Carlini, Nicholas, et al. · arXiv (Cornell University) · 486
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Privacy-Preserving Machine Learning: Threats and Solutions
2019 · Mohammad Al-Rubaie, J. Morris Chang · IEEE Security & Privacy · 443
SoK: Towards the Science of security and privacy in machine learning
2018 · PAPERNOT, Nicolas, MCDANIEL, Patrick, et al. · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · 428
A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View
2018 · Qiang Liu, Pan Li, et al. · IEEE Access · 421
A Review on Digital Twin Technology in Smart Grid, Transportation System and Smart City: Challenges and Future
2023 · Mina Jafari, Abdollah Kavousi-Fard, et al. · IEEE Access · 411
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Attack of the Tails: Yes, You Really Can Backdoor Federated Learning
2020 · Wang, Hongyi, Sreenivasan, Kartik, et al. · arXiv (Cornell University) · 406
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Systematic Poisoning Attacks on and Defenses for Machine Learning in Healthcare
2014 · Mehran Mozaffari-Kermani, Susmita Sur-Kolay, et al. · IEEE Journal of Biomedical and Health Informatics · 383
Artificial Intelligence Safety and Security
2018 · Chapman & Hall/CRC eBooks · 363
Making machine learning robust against adversarial inputs
2018 · Ian Goodfellow, Patrick McDaniel, et al. · Communications of the ACM · 359
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Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses
2022 · Micah Goldblum, Dimitris Tsipras, et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence · 353
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On Safeguarding Privacy and Security in the Framework of Federated Learning
2020 · Chuan Ma, Jun Li, et al. · IEEE Network · 317
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Adversarial Robustness Toolbox v1.0.0
2018 · Nicolae, Maria-Irina, Sinn, Mathieu, et al. · arXiv (Cornell University) · 314
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Adversarial Attacks and Defences Competition
2018 · Alexey Kurakin, Ian Goodfellow, et al. · ˜The œSpringer series on challenges in machine learning · 314
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Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain
2021 · Ishai Rosenberg, Asaf Shabtai, et al. · ACM Computing Surveys · 293
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Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection
2017 · Ambra Demontis, Marco Melis, et al. · IEEE Transactions on Dependable and Secure Computing · 292
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A Comprehensive Survey on Poisoning Attacks and Countermeasures in Machine Learning
2022 · Zhiyi Tian, Lei Cui, et al. · ACM Computing Surveys · 271
A taxonomy and survey of attacks against machine learning
2019 · Nikolaos Pitropakis, Emmanouil Panaousis, et al. · Computer Science Review · 266
Trusting artificial intelligence in cybersecurity is a double-edged sword
2019 · Mariarosaria Taddeo, Tom McCutcheon, et al. · Nature Machine Intelligence · 259
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Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and the Way Forward
2020 · Adnan Qayyum, Muhammad Usama, et al. · IEEE Communications Surveys & Tutorials · 257
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Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS
2020 · Felix O. Olowononi, Danda B Rawat, et al. · IEEE Communications Surveys & Tutorials · 243
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Works count: 2,035
Citation count: 28,860
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