guardd

Guardd Behavioral anomaly detection for Linux, powered by eBPF and machine learning. Guardd learns how a Linux host normally behaves and flags activity that differs from that baseline. Kernel sensors collect process executions and outbound TCP connection attempts; an Isolation Forest scores the...

burp-ResponseClusterer

ResponseClusterer This extension clusters similar responses together, and shows a summary with one request/response per cluster. This allows the tester to get an overview of the tested website's responses from all Burp Suite tools. This is powerful, because it adds an additional vulnerability...

sentrygrid

SentryGrid ML-driven threat detection and continuous monitoring platform built for federal zero-trust architectures. OSCAL-native, FedRAMP Moderate-aligned, hash-chained audit, three-model anomaly ensemble. Quick start make setup install bun + python deps make dev-up bring up the local stack via...

PortDog

PortDog PortDog is a network anomaly detector aimed to detect port scanning techniques. It is entirely written in python and has easy-to-use interface. It was tested on Ubuntu 15. Please note that, it is not working on Windows OS due to suffering from capturing RAW packets.I am working on to writ...

batea

Batea Una batea es una bandeja grande y poco profunda de madera o hierro utilizada tradicionalmente por los buscadores de oro para lavar arena y grava y recuperar pepitas de oro. Batea es un marco de clasificación de dispositivos de red basado en contexto que utiliza la familia de algoritmos de...

msnm-sensor

Descripción MSNM-S Sensor de Monitoreo de Red Estadístico Multivariante muestra la aplicabilidad práctica de los enfoques encontrados en los trabajos de PCA-MSNM y PCA-MSNM Jerárquico. El primero presenta el enfoque MSNM y una nueva metodología estadística multivariante para la detección de...

Merlion

Merlion: A Machine Learning Library for Time Series Table of Contents 1. Introduction 2. Comparison with Related Libraries 3. Installation 4. Documentation 5. Getting Started 1. Anomaly Detection 2. Forecasting 6. Evaluation and Benchmarking 7. Technical Report and Citing Merlion Introduction...

tsusen

Tsusen 津波センサー es un sensor de red independiente diseñado para recopilar información del tráfico regular proveniente del exterior es decir, Internet de forma diaria p. ej., escaneos masivos, escáneres de servicios, etc.. Cualquier anomalía debe ser observada de cerca, ya que puede convertirse en u...

pyod

.. image:: https://raw.githubusercontent.com/yzhao062/pyod/master/brand/pyod-icon.svg :target: https://pyod.dev :alt: PyOD Ecosystem :width: 84px Python Outlier Detection PyOD 3 PyOD 3: Agentische Anomalieerkennung im großen Maßstab |badgewebsite| |badgepypi| |badgeanaconda| |badgedocs|...

ai-security-battle

🤖 AI Security Battle System Red Team vs Blue Team: Adversarial ML Combat Simulation A comprehensive adversarial machine learning security simulation where Red Team attackers battle against Blue Team defenders in a 30-minute AI/ML security warfare scenario. 🎯 Project Overview This system simulates...

netsentryx

NetSentryx PRO 🛡️ AI-Driven, High-Performance Real-Time Network Observability & Threat Detection Engine NetSentryx PRO is a next-generation, event-driven network monitoring and threat intelligence platform. Combining rule-based signatures with a PyTorch Deep Autoencoder, it provides sub-10ms packe...

WindowsMemPageDelta

Windows Executable Memory Page Delta Reporter A Windows Service to performantly produce telemetry on new or modified Windows memory pages that are now executable every 30 seconds. Released as open source by NCC Group Plc - http://www.nccgroup.com/ Developed by Ollie Whitehouse, ollie dot whitehou...

home_SOC_suite

Home SOC Suite — Night's Watch A personal Security Operations Center SOC built from scratch during an Anwendungsentwicklung retraining program. This suite monitors a Windows endpoint for network anomalies, resource spikes, process execution, registry changes, Security Event Log activity, schedule...

Adaptive-Shot Hybrid Quantum Anomaly Detection for Tactile Internet Security: Reliability-Aware Measurement Allocation under Resource Constraints

Tactile Internet TI security analytics must balance reliable thresholded decisions with constrained computational and measurement resources. We study this tension for finite-shot hybrid quantum anomaly inference and introduce the Adaptive-Shot Variational Quantum Circuit AS-VQC policy. This...

Physics-Attested Federated Learning: Securing Collaborative Anomaly Detection in Critical Water Infrastructure

Federated learning enables industrial operators to train shared intrusion detection models without disclosing proprietary operational telemetry. However, existing defenses operate strictly in update space, leaving aggregators blind to data poisoning; model updates derived from fabricated telemetr...

Towards Understanding LLM-Based Log Anomaly Detection: An Empirical Study of Performance, Efficiency, and Robustness

Large language models LLMs have demonstrated promising performance in log anomaly detection, yet how their adaptation strategies, architectures, and deployment configurations affect detection effectiveness remains insufficiently understood. To investigate these factors, we conduct a systematic...

Improving the Reliability of Anomaly Detection for Encrypted OPC UA Traffic over Private 5G

Open Platform Communications Unified Architecture OPC UA is increasingly deployed over private 5G networks in industrial environments, where end-to-end encryption prevents payload inspection by network-based intrusion detection systems IDSs. Although payload-agnostic statistical features extracte...

SAGEGAN: Style-Based Anomaly Detection with Gaussian Embeddings Using Generative Adversarial Networks

Malware evolves faster than rule-based and signature-driven detection pipelines. This paper presents SAGEGAN, a benign-only trained malware anomaly detection framework that converts portable executable files into compact three-channel images and models benign structure through style-conditioned...

Confidence-Guided Cross-Modal Knowledge Transfer for Multimodal Anomaly Detection in Microservice Systems

Accurate anomaly detection is essential for reliable and secure operations of microservice systems. While an increasing number of studies have shifted from unimodal modeling to multimodal interaction and fusion, effectively leveraging reliable cross-modal information remains challenging. The...

Comparative Evaluation of Static Embedding Models for HTTP Request Anomaly Detection

Web applications are increasingly targeted by cyberattacks that exploit HTTP requests to evade security mechanisms. Traditional web application firewalls WAFs rely on rule-based approaches that often exhibit high false positive rates and limited adaptability. Recent studies have explored machine...