Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)

PhD/MSc course on Machine Learning Security (Univ. Cagliari)

A Python library for Secure and Explainable Machine Learning

Paper collection of federated learning. Conferences and Journals Collection for Federated Learning from 2019 to 2021, Accepted Papers, Hot topics and good research groups. Paper summary

The official implementation of the CCS'23 paper, Narcissus clean-label backdoor attack -- only takes THREE images to poison a face recognition dataset in a clean-label way and achieves a 99.89% attack success rate.

A Survey of Poisoning Attacks and Defenses in Recommender Systems

Tensorflow implementation of TrialAttack (Triple Adversarial Learning for Influence based Poisoning Attack in Recommender Systems. KDD 2021)

Tensorflow implementation of APT (Fight Fire with Fire: Towards Robust Recommender Systems via Adversarial Poisoning Training. SIGIR 2021)

Taller de Adversarial Machine Learning

Poisoning attack methods against adversarial training algorithms

Continuous Integration And Continuous Delivery Poisoning Guides

Hack tool for local network: Man in the middle, hosts scan, ARP poisoning, Router and DNS Poisoning

FedAnil is a secure blockchain-enabled Federated Deep Learning Model to address non-IID data and privacy concerns. This repo hosts a simulation for FedAnil written in Python.

FedAnil+ is a novel lightweight, and secure Federated Deep Learning Model to address non-IID data, privacy concerns, and communication overhead. This repo hosts a simulation for FedAnil+ written in Python.

Indirect Invisible Poisoning Attacks on Domain Adaptation

Tensorflow implementation of TrialAttack (Triple Adversarial Learning for Influence based Poisoning Attack in Recommender Systems. KDD 2021)

Test tool to simulate two types of poisoning attack on AI model

Tensorflow implementation of TrialAttack (Triple Adversarial Learning for Influence based Poisoning Attack in Recommender Systems. KDD 2021)

Marco Anisetti, Claudio A. Ardagna, Alessandro Balestrucci, Nicola Bena, Ernesto Damiani, Chan Yeob Yeun. On the Robustness of Random Forest Against Data Poisoning: An Ensemble-Based Approach