Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning

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Items 101–200 of 861

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11. Subpopulation Data Poisoning Attacks
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18. LFighter: Defending against the label-flipping attack in federated learning
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19. Coordinated Backdoor Attacks against Federated Learning with Model-Dependent Triggers
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21. PDGAN: A Novel Poisoning Defense Method in Federated Learning Using Generative Adversarial Network
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22. A Systematic Review of Adversarial Machine Learning Attacks, Defensive Controls and Technologies
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29. On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping
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30. Poisoning attacks and countermeasures in intelligent networks: Status quo and prospects
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32. Membership Inference Attacks Against Recommender Systems
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40. A Detailed Survey on Federated Learning Attacks and Defenses
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43. Security and Privacy for Artificial Intelligence: Opportunities and Challenges
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44. Detecting and mitigating poisoning attacks in federated learning using generative adversarial networks
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45. Toward Federated Large Language Models: Motivations, Methods, and Future Directions
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46. Secure and robust machine learning for healthcare: A survey
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47. Security of Cloud-Based Medical Internet of Things (MIoTs)
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48. Big Data Analytics for Large Scale Wireless Networks: Challenges and Opportunities
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49. Generative Adversarial Networks: A Survey on Attack and Defense Perspective
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52. Intelligent Edge Computing: Security and Privacy Challenges
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55. Dynamic defense against byzantine poisoning attacks in federated learning
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57. SAFELearning: Secure Aggregation in Federated Learning With Backdoor Detectability
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58. Pocket diagnosis: Secure federated learning against poisoning attack in the cloud
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67. Romoa: Robust Model Aggregation for the Resistance of Federated Learning to Model Poisoning Attacks
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68. Data Poisoning Attacks in Internet-of-Vehicle Networks: Taxonomy, State-of-The-Art, and Future Directions
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69. Enhanced Security and Privacy via Fragmented Federated Learning
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70. Network and cybersecurity applications of defense in adversarial attacks: A state-of-the-art using machine learning and deep learning methods
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72. Privacy and Trust Redefined in Federated Machine Learning
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73. Data Poisoning Attacks and Defenses to Crowdsourcing Systems
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75. Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder
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76. Poisoning Attacks with Generative Adversarial Nets
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80. Detection Tolerant Black-Box Adversarial Attack Against Automatic Modulation Classification With Deep Learning
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81. Data Poisoning Attacks in Contextual Bandits
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82. Certified Robustness of Nearest Neighbors against Data Poisoning and Backdoor Attacks
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83. Manipulating SGD with Data Ordering Attacks
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84. CAN-LOC: Spoofing Detection and Physical Intrusion Localization on an In-Vehicle CAN Bus Based on Deep Features of Voltage Signals
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85. Toward Securing Federated Learning Against Poisoning Attacks in Zero Touch B5G Networks
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86. Security of Distributed Intelligence in Edge Computing: Threats and Countermeasures
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90. Secure Federated Learning for Cognitive Radio Sensing
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91. Unraveling Attacks to Machine-Learning-Based IoT Systems: A Survey and the Open Libraries Behind Them
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92. Crowdsourcing Under Data Poisoning Attacks: A Comparative Study
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95. FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks
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97. A Survey of Attacks Against Twitter Spam Detectors in an Adversarial Environment
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98. Edge Learning
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99. Threats on Machine Learning Technique by Data Poisoning Attack: A Survey
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100. Data Poisoning Attacks to Local Differential Privacy Protocols
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