FedDefender is a novel defense mechanism designed to safeguard Federated Learning from the poisoning attacks (i.e., backdoor attacks).
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Updated
May 22, 2024 - Python
FedDefender is a novel defense mechanism designed to safeguard Federated Learning from the poisoning attacks (i.e., backdoor attacks).
Official Website of https://github.com/tamlhp/awesome-recsys-poisoning
A Survey of Poisoning Attacks and Defenses in Recommender Systems
[UbiComp/IMWUT '23] Hierarchical Clustering-based Personalized Federated Learning for Robust and Fair Human Activity Recognition
Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers: Verification and Testing (university project for Cybersecurity)
Source code for our paper "Leveraging Diffusion-Based Image Variations for Robust Training on Poisoned Data" (NeurIPS 2023 Workshop).
My experiments in weaponizing ONOS applications (https://github.com/opennetworkinglab/onos)
This repo contains the implementation for PORE: Provably Robust Recommender Systems against Data Poisoning Attacks
Venom is an ARP-Poisoner that sniffs TLS requests to take advantage of SNI Leak and display all targets DNS traffic even if it is encrypted.
A repository to quickly generate synthetic data and associated trojaned deep learning models
Adversarial-Attacks-and-Defence
A Semi-supervised learning model (Ladder Network) to classify MNIST digits. A few attacks were executed on it with the target of misclassifying 4s with 9s.
Membership inference attacks on (poisoned) segmentation models; master's thesis
Research work on biometric security and template updation using Machine Learning.
Can Adversarial training defend against Poisoning attacks?
Simulation of FL in python for Digit Recognition ML model. Simulated poisoning attacks and studies their impact.
Source code for the Energy-Latency Attacks via Sponge Poisoning paper.
Code for "Biometric Backdoors: A Poisoning Attack Against Unsupervised Template Updating"
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