Intrusion Prevention System to dynamically add firewall rules to block malicious traffic detected by IDS system implemented on Software Defined Networl (SDN). Alternatively, the malicious traffic can be redirected to a Honeypot Server. OpenFlow protocol used for SDN. Snort used for IDS (Intrusion Detection System).

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aka GENESIDS: Reads and parses rules using a "snort like" syntax and generates and sends packets that trigger events in signature based IDS (Intrusion Detection Systems) using these rules

DistributedNIDS-Framework is an open-source distributed intrusion detection system, combining the Snort engine with Apache Spark's data processing capabilities. It uses Docker and Nginx for load balancing and scalability, providing real-time monitoring, alerting, and a web management interface to help users efficiently manage network security.

✓ Using scapy and nmap tools, find out ip/port, ARP SPOOFING & TCP SESSION HIJACKING ✓ Using snort tool provided Intrusion Detection System (IDS). Also, how to bypass this security.

This work focuses on the detection of XSS attack using intrusion detection system. Here attack signature is utilized to detect XSS attack. To test the usefulness and effectiveness of proposed work a proof of concept prototype has been implemented using SNORT IDS. It is observed that proposed system correctly detected XSS attack.

I have developed an Intrusion Detection and Prevention System with the help of SNORT installed in victim machine by writing the customized rules for detecting and preventing the live intrusions that are being attacked from Kali Linux on the victim machine (Ubuntu). Here the intrusions are attacked by kali Linux ( Attacker Machine ) on Ubuntu 20.04 ( Victim Machine)

network intrusion detection system using snort documentation, experiment task as part of CodeAlpha cybersecurity internship.

This is an advanced Intrusion Detection System (IDS) that leverages Artificial Intelligence (AI) and Snort to detect network anomalies in real time. The system is designed to enhance cybersecurity by identifying malicious traffic, unauthorized access, and potential cyber threats using machine learning models and signature-based detection.

This project aims to create a Snort-based Intrusion Detection System (STS) using Docker. The project simulates various attack scenarios to detect abnormal network traffic and potential attacks, and provides package management with a user-friendly interface.

AI-powered Network Intrusion Detection System (NIDS) using Machine Learning to classify network traffic as normal or malicious. Features Snort IDS integration, Logistic Regression model trained on NSL-KDD data, and a Flask API with web interface for real-time traffic analysis. Built with Python, scikit-learn, Snort, and Flask.

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Secure Network & IDS: Simulated cybersecurity threats like DDoS and ARP spoofing in a virtual lab. Implemented a mesh network topology with internal DNS, traffic logging, and an intrusion detection system (IDS) using tools like Wireshark and Snort for real-time threat monitoring.

You will prepare a web server on the latest version of Ubuntu (a Linux distribution) along with necessary security tools to protect it from popular at tacks using the most popular firewall (IPTables) and intrusion detection system (Snort). Note that the web server is also a SSH server, so you are required to install OpenSSH, and allow SSH traffic to go through as well. You will install, configure, and implement your designed policies using these two security tools. You are required to install LAMP (Linux, Apache, MySQL, PHP stack) on Ubuntu with necessary configurations suitable for your design and implementation. You will write two shell scripts to auto mate the installation and configuration of your system for disaster recovery purposes with comments for every single command: installation.sh: It will include all the commands regarding installation of all the necessary services and tools. Also, all confi guration scripts for LAMP. implementation.sh: It will include all the commands regarding implementing policies for your firewall, IDS, etc. You may use a stream editor such as “sed” to implement them using your automated scripts, if that requires editing s pecific files.

This project demonstrates the setup of a Snort-based Intrusion Detection System (IDS) on Windows. It monitors HTTP traffic and identifies SQL Injection, XSS attacks, and Directory Traversal attempts. With custom rules and real-time monitoring, this IDS serves as a powerful tool for web security assessments.

we have created a project using gns3 and pfsense and snort to create an intrusion detection system on a virtual windows machine.

Enterprise-grade IDS combining rule-based intrusion detection with powerful data visualization. Detects DDoS attacks, port scans, and packet flooding in real-time while providing intuitive Splunk dashboards for security analysts. Includes controlled attack simulation environment for cybersecurity education.

This project demonstrates the integration of Snort (a Network Intrusion Detection System) and Fail2Ban (a log-based Intrusion Prevention tool) for real-time detection and automated banning of malicious IP addresses in a simulated environment.

A network-based intrusion detection system using Snort to monitor and detect suspicious activities.

Accelerating Aho-Corasick Algorithm using Odd-Even Sub Patterns to improve Snort Intrusion Detection System

🛡️ Network Intrusion Detection and Prevention System using Snort with custom rules for real-time attack detection and logging.

This report briefly explains the using snort box as intrusion prevention and detection system. Snort is installed on Ubuntu 14.04 machine with BASE, Barnyard2 and MySQL.

Creating a single rule to detect "all TCP port 80 traffic" packets using SNORT that is a free, open-source network intrusion detection system (IDS) and intrusion prevention system (IPS)

Network Intrusion Detection System Develop a network-based intrusion detection system using tools like Snort or Suricata. Set up rules and alerts to identify and respond to suspicious network activity. You can even visualize the detected attacks.

Virtualized Intrusion Detection System (IDS) with Snort A Wi-Fi-based IDS simulation using Kali Linux (attacker), Ubuntu (victim), and a Snort-enabled Ubuntu VM to monitor and detect cyberattacks — all within a multi-VM, cross-host setup.

AI-powered network intrusion detection system — uses Isolation Forest anomaly detection to analyze live packet captures from Wireshark and flag abnormal traffic patterns that signature-based tools like Snort typically miss.

BBQ reads rules of the Snort Intrusion Detection System (IDS), visualizes them in a table and allows firing them to trigger alerts. This can be used to test Snort's performance and to find computationally expensive rules.

is a Python-based tool designed to work with Snort, the popular open-source intrusion detection and prevention system (IDS/IPS). It provides a way to interact with Snort logs, parse alerts, and automate security monitoring tasks using Python scripts

Develop a network-based intrusion detection system using tools like Snort or Suricata. Set up rules and alerts to identify and respond to suspicious network activity. You can even visualize the detected attacks.

This repository contains code used to run a modified KMeans clustering algorithm with additive secret sharing on Snort Alert data in order to accomplish a collaborative privacy preserving approach to intrusion detection systems.