Overview
This project, developed by Team SecureNet Innovators, provides a benchmarking tool for evaluating the performance of Intrusion Detection and Prevention Systems (IDS/IPS) based on key metrics like throughput, latency, and detection accuracy. The repository also provides a sample IDS/IPS solution under varied conditions and uses metrics aligned with the methodologies outlined in RFC 9411: Benchmarking Methodology for Network Security Device Performance.

Objectives
The tool benchmarks custom IDS/IPS devices across:

Throughput
Latency
Detection Accuracy
Key Features
Traffic Profiles: Supports various traffic conditions, including regular and attack traffic profiles.
Signature Complexity: Analyze how the complexity of signatures affects IDS/IPS performance.
Latency and Packet Drop Measurement: Provides real-time insights on packet drop and latency variations as traffic increases.
Adaptive Traffic Scaling: Dynamically adjusts traffic to simulate real-world load spikes for stress testing.
Machine Learning Analysis: Uses ML models to predict failure points and optimize IDS/IPS configuration.
Interactive Visualizations: These are real-time visualizations of throughput, latency, and accuracy, allowing for deeper performance insights.