ESB, SOA, REST, APIs and Cloud Integrations in Python

A pytorch adversarial library for attack and defense methods on images and graphs

A curated collection of adversarial attack and defense on graph data.

Cisco Config Analysis Tool

h2t (HTTP Hardening Tool) scans a website and suggests security headers to apply

Implementation of the KDD 2020 paper "Graph Structure Learning for Robust Graph Neural Networks"

A certifiable defense against adversarial examples by training neural networks to be provably robust

Python toolbox to evaluate graph vulnerability and robustness (CIKM 2021)

Emulate and Dissect MSF and *other* attacks

SHIELD: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression

Feature Scattering Adversarial Training (NeurIPS19)

An application to catch, search and analyze HTTP secure headers.

This is the official pytorch implementation for paper: IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration

[ICML 2019] ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation

EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples

FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning [ICLR‘23, Best Paper Award at ECCV’22 AROW Workshop]

Deauthalyzer is a script designed to monitor WiFi networks and detect deauthentication attacks. It utilizes packet sniffing and analysis techniques to identify deauthentication attack packets and provide relevant information about the attack.

A defense tool - detect web shells in local directories via md5sum

The Web Armory | Web武器库

Keras with Tensorflow implementation of our paper "Mockingbird: Defending Against Deep-Learning-Based Website Fingerprinting Attacks with Adversarial Traces" which is published in IEEE Transactions on Information Forensics and Security (TIFS).