anomaly-flow

Anomaly-Flow Framework for implementing Intrusion Detection Systems NIDS aimed at identifying anomalies in network flows using Machine Learning models. Reference paper: Anomaly-Flow: A Multi-domain Federated Generative Adversarial Network for Distributed Denial-of-Service Detection Create the...

foolbox

home: true heroImage: /logo.png heroText: Foolbox tagline: "Foolbox: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX" actionText: Get Started → actionLink: /guide/ features: title: Native Performance details: Foolbox 3 is built on to...

pulsar

PULSAR Protocol Learning, Simulation and Stateful Fuzzer Pulsar is a network fuzzer with automatic protocol learning and simulation capabilites. The tool allows to model a protocol through machine learning techniques, such as clustering, and Markov models. These models can be used to simulate...

Manati

ManaTI Project Machine Learning for Threat Intuitive Analysis The goal of the ManaTI project is to develop machine learning techniques to assist an intuitive threat analyst to speed the discovery of new security problems. The machine learning will contribute to the analysis by finding new...

NGWAF

N3XT-G3N Web App Firewall NGWAF NGWAF is created by@yupengfei, @zhangbosen, @matthewng and @elizabethlim. Special shoutout to@ruinahkoh for her contributions to the initial stages of NGWAF. NGWAF is an experimental proof of concept and not ready for production use in its current form. Contents 1...

uriDeep

uriDeep Unicode encoding attacks with machine learning. Tool based on machine learning to create amazing fake domains using confusables. Some domains can deceive IDN policies Chrome & Firefox. I created the best big dictionary of confusables using neural networks. It is used in the tool and it ca...

Apkx-Hunter

APKX-Hunter v3.0.0 -- Debian Package APKX-Hunter at Black Hat Arsenal Europe 2026 View APKX-Hunter on Black Hat Europe Arsenal APKX-Hunter is an open-source Android Static Analysis Framework written entirely in C , purpose-built for Android security assessments, reverse engineering, malware...

deep-pwning

Deep-pwning is a lightweight framework for experimenting with machine learning models with the goal of evaluating their robustness against a motivated adversary. Note that deep-pwning in its current state is no where close to maturity or completion. It is meant to be experimented with, expanded...

WAF-A-MoLE

WAF-A-MoLE A guided mutation-based fuzzer for ML-based Web Application Firewalls, inspired by AFL and based on the FuzzingBook by Andreas Zeller et al. Given an input SQL injection query, it tries to produce a semantic invariant query that is able to bypass the target WAF. You can use this tool f...

backdoor-learning-resources

Backdoor Learning Resources This Github repository summarizes a list of Backdoor Learning resources. For more details and the categorization criteria, please refer to our survey. We will try our best to continuously maintain this Github Repository in a monthly manner. Why Backdoor Learning?...

SyntheticSun

SyntheticSun SyntheticSun is a defense-in-depth security automation and monitoring framework which utilizes threat intelligence, machine learning, managed AWS security services and, serverless technologies to continuously prevent, detect and respond to threats. You sleep in fragmented glass With...

SharpML

SharpML SharpML is a proof of concept file share data mining tool using Machine Learning in Python and C. The tool is discussed in more detail on our blog here, but is summarised below also: SharpML is C and Python based tool that performs a number of operations with a view to mining file shares,...

pcfg_cracker

PCFG = Probabilistic Context Free Grammar PCFG = Pretty Cool Fuzzy Guesser In short: A collection of tools to perform research into how humans generate passwords. These can be used to crack password hashes, but also create synthetic passwords honeywords, or help develop better password strength...

robustbench

RobustBench: a standardized adversarial robustness benchmark Francesco Croce University of Tübingen, Maksym Andriushchenko EPFL, Vikash Sehwag Princeton University, Edoardo Debenedetti EPFL, Nicolas Flammarion EPFL, Mung Chiang Purdue University, Prateek Mittal Princeton University, Matthias Hein...

CVE-2024-38063

CVE-2024-38063 Windows Vulnerability that allows the implementation of unauthorised activity over the network: remote execution of Windows TCP/IP remote code. Two virtual machines - a victim and an attacker - are ready to simulate the attack. On the victim machine, a tool for capturing network...

ai-exploits

AI Exploits The AI world has a security problem and it's not just in the inputs given to LLMs such as ChatGPT. Based on research done by Protect AI and independent security experts on the Huntr Bug Bounty Platform, there are far more impactful and practical attacks against the tools, libraries an...

network-threats-taxonomy

Network Threats And Tools Taxonomy Machine Learning based Intrusion Detection Systems are difficult to evaluate due to a shortage of datasets representing accurately network traffic and their associated threats. In this project we attempt at solving this problem by presenting two taxonomies "A...

logparser

Logparser Logparser provides a machine learning toolkit and benchmarks for automated log parsing, which is a crucial step for structured log analytics. By applying logparser, users can automatically extract event templates from unstructured logs and convert raw log messages into a sequence of...

API-attack-detection-with-AI

Detection of SQL/XSS Attacks on Web API with Artificial Intelligence / Machine Learning This project aims to detect SQL and XSS injections in API requests/params using machine learning. Project Overview This project aims to detect SQL and XSS injections in API requests and parameters using Machin...

ache

ACHE Focused Crawler ACHE is a focused web crawler. It collects web pages that satisfy some specific criteria, e.g., pages that belong to a given domain or that contain a user-specified pattern. ACHE differs from generic crawlers in sense that it uses page classifiers to distinguish between...