a network traffic anomaly detector that captures and analyzes network packets to identify anomalous behavior. It uses machine learning techniques to detect deviations from normal traffic patterns // un detector de anomalías en el tráfico de red que captura y analiza paquetes de red para identificar comportamientos anómalos.

NetGuard: Network Traffic Anomaly Detector

A network traffic anomaly detector application that uses data relating to the network traffic amount to find anomalies in the amount of traffic for a given checkpoint. Here, we check for spikes in network transfer over time using DetectSpikeBySsa.

This repository contains the source code of the entropy-based network traffic anomaly detector. It was developed as part of the activities of the research project "Performance Evaluation of Entropy-based Algorithms for Network Traffic Anomaly Detection in Cloud Computing Systems" at the Research Group of Convergent Networks (GPRC) from the Federal Institute of Education, Science and Technology of Paraíba, Campina Grande Campus, Paraíba, Brazil. The participants thank CNPQ agency for funding the project. Developers: Eduardo Jerônimo and Aleciano Lobo. Manager/Advisor: Ana Cristina Oliveira

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A Python-based Network Traffic Anomaly Detection system for Blue Team / SOC operations

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A machine learning + security project that detects unusual network traffic patterns.

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The Network Traffic Anomaly Detector is a Python-based real-time monitoring tool designed to analyze network traffic for suspicious and potentially malicious behavior. It uses packet inspection (via Scapy) and a set of customizable detection rules to identify threats related to confidentiality, integrity, and authenticity.

This is an AI-based Intrusion Detection System that captures live network packets using Scapy, extracts traffic features, and uses an Isolation Forest machine learning model to detect anomalous or suspicious traffic patterns in real time. It also includes a Streamlit dashboard for visualization.

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Real-time anomaly detector for suspicious network traffic.

Artificial Immune System (AIS) anomaly detector for IoMT network traffic

NetWatch is a Python-based network traffic anomaly detector and more.

Beaconing anomaly detector for C2-like network traffic periodicity and drift.

The AI-Enhanced Cybersecurity Threat Detector is a web-based platform designed to visualize and monitor potential cyber threats using AI-powered anomaly detection. It leverages transformer models to analyze system logs and network traffic for early threat identification.

Python-based network traffic anomaly detector - behavioral baselining, isolation Forest ML detection, and MITRE ATT&CK mapping.

Real-time network traffic monitor & anomaly detector — detects port scans, bandwidth spikes, suspicious IPs and protocol anomalies with a SOC-style dashboard

An interactive Network Anomaly Detector built with Python and Streamlit. Uses a Random Forest model to classify cybersecurity threats from network traffic data.

A Python-based Network Traffic Analyzer & Anomaly Detector that captures and visualizes network traffic, analyzes protocols, and alerts on spikes, new hosts, or port scans. Inspired by SOC workflows and built using cybersecurity concepts from Palo Alto Networks certifications.

An AI-powered network anomaly detector that gathers live traffic, derives features and then with the help of Isolation Forest identifies malicious or suspicious traffic in real-time with a visual representation in a Streamlit dashboard.

Developed a Zero-Day Exploit Detector on Kali Linux, analyzing real-time network traffic and identifying 10% of packets as potential threats using anomaly detection (Python, Scapy, Scikit-learn).

The Zero-Day Network Intrusion Detector is a machine learning–based security system designed to identify previously unseen (zero-day) network attacks by analyzing abnormal patterns in network traffic. Unlike signature-based intrusion detection systems, this project focuses on behavioral and anomaly-based detection

This project analyzes network traffic using ML models like Isolation Forest, Autoencoders, and LSTM-based detectors to identify anomalies, cyber-attacks, and suspicious patterns. It provides preprocessing, visualization, real-time detection, and detailed security insights.

Network Intrusion Detector is a distributed intrusion detection system built with PySpark. It preprocesses, encodes, and models network traffic data to detect anomalies using a Random Forest classifier, achieving high accuracy and efficiency through feature selection and scalable data processing. The system is suitable for large-scale environments

Network Intrusion Detector is a verifiable binary logistic regression classifier deployed on the OpenGradient blockchain. Detect potential network intrusion attempts from five traffic anomaly signals. It accepts 5 normalised numerical features as a flat array and outputs a single probability score between 0 and 1 via a sigmoid activation function

Synthetic, yet realistic. This repository shows how Large Language Models (LLMs) can be coaxed into speaking the language of a network packet, producing traffic that mirrors real‑world IoT deployments. The resulting traces can be used to stress‑test Intrusion‑Detection Systems (IDS), benchmark anomaly‑detectors, or augment scarce training data.