network-traffic-analysis
There are 39 repositories under network-traffic-analysis topic.
cisagov/Malcolm
Malcolm is a powerful, easily deployable network traffic analysis tool suite for full packet capture artifacts (PCAP files), Zeek logs and Suricata alerts.
idaholab/Malcolm
Malcolm is a powerful, easily deployable network traffic analysis tool suite for full packet capture artifacts (PCAP files), Zeek logs and Suricata alerts.
activecm/rita
Real Intelligence Threat Analytics (RITA) is a framework for detecting command and control communication through network traffic analysis.
IQTLabs/packet_cafe
A platform built for easy-to-use automated network traffic analysis
stratosphereips/AIP
The Attacker IP Prioritizer(AIP) dynamically generates resource-friendly IPv4 blocklists from Zeek network flows.
Ravi-Teja-konda/Network_traffic_analyzer
A Python-based network traffic analyzer for PCAP files, providing insights into protocol distribution, IP communications, and potential port scanning activities.
mmguero-dev/Malcolm
Malcolm is a powerful, easily deployable network traffic analysis tool suite for full packet capture artifacts (PCAP files), Zeek logs and Suricata alerts.
Bishal77/Hybrid-CNN-BiLSTM-architecture-for-detecting-multi-step-cyber-attack
The model leverages the strengths of both CNNs and BiLSTM networks to effectively capture spatial and temporal patterns in network traffic data. We trained and evaluated the model using a comprehensive dataset of cyber attacks. The model achieved a high accuracy of 99%.
jackm-g/nettraffic-ml-notes
Notes for technologies useful in applying ml to the unsw-nb15 dataset (Draft)
OpenIxia/nas-cloud-demo
Keysight NAS (IXIA) Cloud Demo Examples
OTARIS/OTAlyzer
OTARIS traffic analyzer
CyberUP-STL/cyber-skyline
Curriculum developed to assist in CyberSkyline challenges
sergio11/blackvenom
BlackVenom is an ethical ARP and DNS spoofing tool 🛡️ designed for cybersecurity professionals, enabling the interception and logging of network traffic 📄 to identify vulnerabilities. It facilitates effective network analysis 🔍 while ensuring stealth and compliance with ethical hacking practices ⚖️.
IdanRosenzweig/Networking-Framework
A comprehensive networking framework designed primarily for high-performance processing of raw packets. Implements most modern protocols (specifically TCP/IP) and further networking software. In addition, the project contains a suite of networking tools built entirely on top of the framework
MaheshShukla1/Snort-IDS-Configuration-Rules-and-Examples
This repository provides comprehensive guides, configurations, rules, and practical examples for Snort, the open-source intrusion detection system (IDS). Ideal for cybersecurity professionals and enthusiasts looking to enhance their network security skills.
makt96/treebased-ids
This project is a live network monitoring dashboard that leverages tree-based machine learning algorithms to detect intrusions in real-time. The system uses Flask and Socket.IO for real-time data updates, and Chart.js for data visualization. The dashboard provides various charts to visualize network data and sends notifications for suspicious activ
0xAminED/APA
An advanced Packet Analyzer written in C that processes PCAP files to analyze network traffic.
4xyy/network_anomaly_detector
A simple, yet powerful Python-based network anomaly detection tool that uses machine learning to analyze network traffic and detect suspicious activity. The tool integrates with the VirusTotal API to check the reputation of anomalous IP addresses.
AreejFatimaz/ThreatGuard-Advanced-Threat-Detection-System
ThreatGuard is an advanced threat detection system that utilizes the CICIDS 2017 dataset for network traffic analysis and anomaly detection.
ericyoc/synthetic_network_traffic_simulation_poc
A simulation of network traffic using synthetic network traffic for 802.11, 3G GSM, 4G LTE, and 5G NR
Khanh779/Network_Packet_Analyzer
The "Network Packet Analyzer" project is a network packet analysis tool, helping to analyze and display information about data packets transmitted over the network.
Soonies/WireSharkBis
Visualisateur graphique de trafic reseau sous forme de graphe de flux
xgr19/Dryad
Dryad: Deploying Adaptive Trees on Programmable Switches for Networking Classification (ICNP2023)
Antonios-Kagias/Computer_Networks_for_Big_Data
Network traffic analysis, traffic characteristics extraction, flow migration and evaluation
CHamilton0/Dissecting-Malware-in-the-Wild
Major project for Advanced Topics in Computer Science. Using mitmproxy to automatically detect if private data has been leaked in network traffic data by certain android applications.
lrmulkayhee/malware-education-repo
This repository provides educational resources and practical examples for understanding and analyzing malware. It includes tutorials, quizzes, presentations, exercises, sample code, and articles that cover various aspects of malware analysis, incident response, and cybersecurity.
shngul/DDos-Attack
Kali Linux sanal makinesi kullanarak DDoS saldırılarının simülasyonunu gerçekleştirip, oluşturulan veri seti üzerinde makine öğrenme algoritmaları ile saldırı tespiti ve normal trafikten ayırma.
xgr19/Loong
Generating neural networks for diverse networking classification tasks via hardware-aware neural architecture search, Transactions on Computers 2023
ZakiRucker/GradSchoolCoding
This is the collection of many of the programming projects from my graduate school studies.
bansal-yash/COL334-Computer-Networks
Course assignments of COL334:- Computer Networks course at IIT Delhi under Professor Tarun Mangla
Recker-Dev/IOT-Healthcare-Network-Traffic-Attack-Predictor
A machine learning project to detect cyberattacks in IoT healthcare networks. Utilizes PCA for dimensionality reduction, data visualization for insights, and ANN for classification. Features a FastAPI backend and Streamlit UI for inference with labeled and unlabeled datasets.
sundramsharma1/Network-Traffic-Analysis
Network Traffic Analysis
Trident09/net-sec-ai-MP
This project predicts network traffic patterns using a machine learning model trained on the CICIDS dataset. It includes a Streamlit app for real-time predictions, displaying predicted labels and probabilities for uploaded CSV data. The project is structured into three parts: dataset, model training, and frontend (Streamlit app).