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Malcolm is a powerful, easily deployable network traffic analysis tool suite for full packet capture artifacts (PCAP files), Zeek logs and Suricata alerts.
Real Intelligence Threat Analytics (RITA) is a framework for detecting command and control communication through network traffic analysis.
Malcolm is a powerful, easily deployable network traffic analysis tool suite for full packet capture artifacts (PCAP files), Zeek logs and Suricata alerts.
Materials about Encrypted Traffic Analysis
SaaS Zero - Network Traffic Monitor Professional network traffic monitoring and security analysis platform
A platform built for easy-to-use automated network traffic analysis
A Python-based network traffic analyzer for PCAP files, providing insights into protocol distribution, IP communications, and potential port scanning activities.
The Attacker IP Prioritizer(AIP) dynamically generates resource-friendly IPv4 blocklists from Zeek network flows.
Malcolm is a powerful, easily deployable network traffic analysis tool suite for full packet capture artifacts (PCAP files), Zeek logs and Suricata alerts.
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%.
Notes for technologies useful in applying ml to the unsw-nb15 dataset (Draft)
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.
Keysight NAS (IXIA) Cloud Demo Examples
Plug into extended SecOps: Bring Google Cloud's analytics to your local network. tshark captures on-prem, GCP transforms to UDM. Scalable, event-driven, via Terraform.
OTARIS traffic analyzer
Flonwix is a graphical network traffic analyzer for Linux-based systems that relies on ptcpdump
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
The project is about fingerprinting operating systems using different multi-class classification algorithms.
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