poisoning
There are 19 repositories under poisoning topic.
Trusted-AI/adversarial-robustness-toolbox
Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
MinghuiChen43/awesome-trustworthy-deep-learning
A curated list of trustworthy deep learning papers. Daily updating...
rhaidiz/dribble
A small project for stealing Wi-Fi passwords via browser's cache poisoning.
byt3n33dl3/NetVenom
P O I S O N I N G
samber/arp-spoofing
💥 Simple implementation of arp poisoning attack ;)
CN-TU/ids-backdoor
Contact: Maximilian Bachl, Alexander Hartl. Explores defenses against backdoors and poisoning attacks for Intrusion Detection Systems. Code for "EagerNet" is in the "eager" branch.
daniel4x/mitm-python
A simple as possible man in the middle written in python using scapy
tntekprod/teaspoof
MITM ARP Cache poisoner implemented with Scapy and also a HTTP sniffer
SAMashiyane/Naloxone
Prediction of naloxone dose in opioids toxicity based on machine learning techniques
vertexclique/avu
Kaminsky DNS cache poisoning tool
Ayushii12/Attacks-On-Federated-Learning
This study explores the vulnerability of the Federated Learning (FL) model where a portion of clients participating in the FL process is under the control of adversaries who don’t have access to the training data but can access the training model and its parameters.
Benwick921/dnscachepoisoning
DNS Cache Poisoning
Vinayak2002/Poisoning_Attacks_in_FL
Simulation of FL in python for Digit Recognition ML model. Simulated poisoning attacks and studies their impact.
hackingyseguridad/dnspoison
dnspoison inyecta respuestas dns con IP host falso
Lane-Affield/DeepFakeCapstone
This is a project by Lane Affield, Emma Gerdeman, and Munachi Okuagu to showcase what we have learned through Drake University's Artificial Intelligence Program
romerixo/venom_arp
Python script for arp spoofing
SESARLab/ensemble-random-forest-robustness-against-poisoning
M. Anisetti, C. A. Ardagna, A. Balestrucci, N. Bena, E. Damiani, C. Y. Yeun. "On the Robustness of Random Forest Against Data Poisoning: An Ensemble-Based Approach". In IEEE TSUSC, vol. 8 no. 4
The-Funk/tinsnip2
Testing 1,2,3
zwt-io/Building-Web-Apps-with-Spring-5-and-Angular
Building Web Apps with Spring 5 and Angular, published by Packt