loglizer

loglizer Loglizer is a machine learning-based log analysis toolkit for automated anomaly detection. Loglizer是一款基于AI的日志大数据分析工具, 能用于自动异常检测、智能故障诊断等场景 Logs are imperative in the development and maintenance process of many software systems. They record detailed runtime information during system...

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,...

Octopii

Octopii ⠀⠀⠀⠀⠀⠀⠀⣤⣤⣄⣀⡀⠀⠀⠀⢀⣠⣤⣤⣄⡀⠀⠀⠀⢀⣀⣠⣤⣤⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠸⣿⣿⡿⠿⢿⣷⡄⢠⣿⣿⣿⣿⣿⣿⡄⢀⣾⡿⠿⢿⣿⣿⠇⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠈⠉⠀⠀⢸⣿⡇⢸⣿⣿⣿⣿⣿⣿⡇⢸⣿⡇⠀⠀⠉⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⣠⣤⡀⠀⠀⠀⠀⠀⠀⠀⢸⣿⡇⢸⣿⣿⣿⣿⣿⣿⡇⢸⣿⡇⠀⠀⠀⠀⠀⠀⠀⢀⣤⣄⠀⠀⠀ ⠸⣿⣿⣿⣿⣿⣿⣿⣿⣦⠀⢸⣿⡇⢸⣿⣿⣿⣿⣿⣿⡇⢸⣿⡇⠀⣴⣿⣿⣿⣿⣿⣿⣿⣿⠇⠀⠀ ⠀⠉⠉⠁⠀⠀⠀⠀⣿⣿⠀⢸⣿⡇⠀⠉⣿⣿⣿⣿⠉⠀⢸⣿⡇⠀⣿⣿⠀⠀⠀⠀⠈⠉⠉⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⣿⣿⣀⣈⣻⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣟⣁⣀⣿⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀...

AIF360

AI Fairness 360 AIF360 The AI Fairness 360 toolkit is an extensible open-source library containing techniques developed by the research community to help detect and mitigate bias in machine learning models throughout the AI application lifecycle. AI Fairness 360 package is available in both Pytho...

machine_learning_security

Machine Learning and Security Source codes about machine learning and security. Line up. Cyber security and Machine Learning course The elementary training course of Machine learning for security engineer. Vulnerabilities of Machine Learning Summary of Machine Learning vulnerability. Analytics...

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...

Pegasus-Malware-Scan

pegasus-malware-scan Description Pegasus Malware Scan is a comprehensive cybersecurity solution designed for advanced threat detection and malware analysis. Built with Python, this powerful tool employs multi-layered detection techniques to identify and neutralize a wide range of malicious...

AIX360

AI Explainability 360 v0.3.0 ✨ NEW: IBM Research has a new In-Context Explainability 360 ICX360 toolkit that extends explainability to LLMs , specifically in terms of the input given to the LLM. Please check it out! 🚀✨ The AI Explainability 360 toolkit is an open-source library that supports...

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?...

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...

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...

stringsifter

StringSifter is a machine learning tool that automatically ranks strings based on their relevance for malware analysis. Quick Links Technical Blogpost - Learning to Rank Strings Output for Speedier Malware Analysis Announcement Blogpost - Open Sourcing StringSifter DerbyCon Talk - StringSifter:...

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...

hexora

hexora Hexora is a static analysis tool designed to detect malicious and harmful patterns in Python code. It combines a rule-based static analysis engine with a machine-learned model that scores entire source files to classify them as malicious or benign. It can be used to: Audit project...

network-attack-detection

Network Attack Detection using Machine Learning Description This project leverages machine learning techniques to classify network attacks such as Port Scanning, Denial of Service DoS, and malware. The input data is in the Netflow V9 format, which is a standard format used by Cisco. The...

ThreatSentry-AI

ThreatSentry AI: AI-Powered Threat Hunting Tool Automate external asset discovery, intelligently enrich with multi-source data, and leverage ensemble machine learning to predict and prioritize risk. Transform alert fatigue into actionable intelligence. 🚀 Mission Statement ThreatSentry AI eliminat...

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...

batea

Batea A batea is a large shallow pan of wood or iron traditionally used by gold prospectors for washing sand and gravel to recover gold nuggets. Batea is a context-driven network device ranking framework based on the anomaly detection family of machine learning algorithms. The goal of Batea is to...

secretflow

简体中文｜English SecretFlow is a unified framework for privacy-preserving data intelligence and machine learning. To achieve this goal, it provides: An abstract device layer consists of plain devices and secret devices which encapsulate various cryptographic protocols. A device flow layer modeling...

OMLASP

OMLASP | | | | | | || | | || | | | | | | | | OMLASP - Open Machine Learning Application Security Project is intended to become a standard for auditing Machine Learning algorithms. It will not only focus on the security of the algorithms but also on their biases. Typically, when applications that...