This project develops and deploys a robust, multi-class Network Intrusion Detection System (IDS) capable of identifying various attack types and normal network traffic. Leveraging a 1D Convolutional Neural Network (CNN) architecture, the system is trained on the comprehensive UNSW-NB15 dataset, which features a wide range of modern attacks.

A research & development project to create and deploy a Network-based Intrusion Detection System (IDS) to detect intruders on a distributed system. That is, it detects and classify threatening or anomalous network traffic as opposed to safe traffic and usage. The project runs on a real-time, distributed cluster on Apache Storm which processes incoming network packets, and uses our novel algorithms and Machine Learning to detect intruders. It uses supervised Machine Learning classifiers such as decision trees, ensemble decision trees, support vector machines, etc. as well as being built with the principles of anomaly-based Intrusion Detection Systems.

This project implements a Hybrid Intrusion Detection System (IDS) using multiple machine learning models to classify network traffic as benign or malicious in real-time. The system utilizes Scapy for packet capture and feature extraction, along with Isolation Forest and a GAN discriminator for anomaly detection

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.

CyberSentinelML is a machine learning-based intrusion detection system (IDS) designed to detect and classify malicious network attacks in real-time. Using advanced ML models, it analyzes network traffic patterns to identify threats such as DDoS, port scanning, malware communication, and unauthorized access attempts.

With the rapid growth of the Internet of Things (IoT) as well as the vast and vital dependence on IoT, serious security risks are also growing. Many factors contribute to these risks. For example, limited resources in terms of computational capabilities, power, and storage make IoT networks highly vulnerable. Securing IoT networks is vital due to the importance and sensitivity of the data collected from the devices and systems. Furthermore, the nature of IoT networks, such as including a large number of devices, limited resources, and traffic heterogeneity between the various IoT networks raises different security challenges. Moreover, some classic security methods become less effective against IoT cyber-attacks, such as cryptography. An urgent need for real-time and lightweight detection of cyber-attacks is needed to secure IoT networks. This demand achieved by a reliable and efficient intrusion detection system (IDS) that can meet the high scalability and dynamicity of IoT environments. This research analyzed the traffic and features of commonly used and recently published datasets for IoT networks. Furthermore, it proposed two feature selection methods. Moreover, it reduced BotNet-IoT dataset dimensionality from 115 features to 23 features, which will speed-up the detection. Furthermore, it analyzed the effects of traffic heterogeneity levels and time-window size on several classification methods to justify the detection model selection. Additionally, it considered different performance metrics to enable comparing results with other works. Regarding BotNet-IoT dataset, we found that few features play a critical role in IDS performance, where larger time-window was slightly better than the shorter time-windows. Furthermore, we found that PCA classifier performance was significantly affected by the traffic heterogeneity, therefore, it is not suitable for IDS in practice. Moreover, the Boosted Tree showed the best and the most stable performance among all the considered classification methods.

AI-Based Intrusion Detection System (IDS) is a cyber-security project that uses machine learning to analyze network traffic and detect malicious activities by classifying traffic as normal or attack in real time.

Real-Time Intrusion Detection System (Live IDS) A modular, Flask-powered real-time Intrusion Detection System (IDS) that captures live network traffic, extracts behavioral features, and classifies packets using a trained machine learning model. Designed for security research, lab automation, and forensic analysis.

This project captures live network traffic using wireshark and analyzes it with a trained neural network. It detects anomalies in real-time, logs suspicious activity, and provides alerts. The system preprocesses network data, classifies traffic, and can be integrated into IDS for enhanced cybersecurity.

A real-time AI-powered Intrusion Detection System (IDS) that uses machine learning to analyze and classify network traffic, detect anomalies, visualize threats, and support forensic investigation using Suricata, Zeek, and Streamlit.

This project implements an AI-driven Intrusion Detection System (IDS) to detect and classify cyber threats in real-time. By leveraging machine learning and deep learning algorithms, this IDS analyzes network traffic patterns to identify malicious activities such as DDoS, port scanning, and brute-force attacks.

Intrusion Detection System (IDS) is a machine learning-based tool designed to identify and classify network threats in real-time. It includes a testing environment with intentionally flawed web applications and analyzes traffic patterns to detect potential intrusions, ensuring network security and resilience.

AI-Based Intrusion Detection System using Deep Learning to detect malicious network activities in real time. Built with Python and Keras, the model learns traffic patterns to classify normal and intrusive behavior, improving detection accuracy and reducing false positives over traditional IDS.

A compact and efficient machine-learning–based intrusion detection system (IDS) designed for edge and IoT environments. This project focuses on real-time detection of network threats such as DDoS, spoofing, port scanning, and anomalous traffic patterns using lightweight feature engineering and optimized ML classifiers.

A deep learning-based IDS that can detect various types of network attacks (DDoS, SQL injection, malware) from network traffic in real-time. Multi-class classifier, low latency detection, attack type identification.

Machine learning–based Network Intrusion Detection System (IDS) using the NSL-KDD dataset, deployed as a FastAPI service to classify network traffic as normal or attack in real time.

Intrusion Detection System (IDS) — Capstone Project This project focuses on detecting and classifying cyber intrusions in real-time network traffic using Machine Learning and Deep Learning models.

This project is a real-time Network Intrusion Detection System (IDS) built using Machine Learning to detect cyberattacks with high accuracy. It is trained on the CICIDS-2017 Dataset and uses the XGBoost classifier to categorize network traffic

Mini-IDS is a flow-based Network Intrusion Detection System using supervised ML (Random Forest) to classify network traffic as benign or malicious. It processes CICIDS2017 dataset for training and will perform real-time inference on live traffic via FastAPI.

This project demonstrates a real-time Intrusion Detection System (IDS) built with machine learning to identify and classify security threats in a Software-Defined Networking (SDN) environment. Using Mininet to simulate network traffic, the IDS monitors packets and raises alerts when malicious activity is detected.

This project presents a Machine Learning-based Intrusion Detection System (IDS) designed to identify and classify network attacks in real-time. The system analyzes network traffic data and detects malicious activities such as DoS, Probe, R2L, and U2R attacks.

The Network Intrusion Detection System (IDS) project aims to develop an efficient, scalable solution for detecting and mitigating security threats in network traffic. By applying machine learning techniques, the system automatically identifies and classifies various types of network intrusions, enabling real-time monitoring and protection

The Network Intrusion Detection System (IDS) project aims to develop an efficient, scalable solution for detecting and mitigating security threats in network traffic. By applying machine learning techniques, the system automatically identifies and classifies various types of network intrusions, enabling real-time monitoring and protection

A real-time anomaly detection system for network traffic using machine learning and a custom Tkinter-based user interface. Trained on the CIC-IDS-2017 dataset, it identifies suspicious patterns using selected network features with the help of random forest classifier along with Scapy for live packet sniffing.

AI-Based Intrusion Detection System (IDS) uses Machine Learning to monitor network traffic and detect cyber threats like DoS, brute force, and unauthorized access in real time. It classifies traffic as normal or malicious and provides alerts with a dashboard using Python, Flask, and MySQL.

This project focuses on building a Machine Learning-based Intrusion Detection System (IDS) for real-time network threat detection using the CICIDS2017 dataset. The model is designed to identify and classify network traffic as either benign or malicious, leveraging various machine learning techniques.

A fully automated IDS that captures and classifies network traffic using machine learning. Utilizes Scapy for packet capture, Keras for classification, and multi-threading for real-time analysis. Features batch processing and efficient data handling with queue structures.

A cloud-based Intrusion Detection System (IDS) using an **Ensemble SVM** to classify network traffic as normal or malicious. It delivers accurate, real-time predictions and is deployable on platforms like Amazon Web Services, Google Cloud Platform, and Microsoft Azure.

AI-IDS is a machine learning-based Intrusion Detection System that captures live network packets, extracts features, and classifies traffic as normal or attack in real time. It provides an interactive web dashboard for labeling, training, and monitoring cybersecurity threats dynamically.

The IDS project aims to detect and classify network attacks like DoS, port scans, and normal traffic in real time. Developed using Python and Scapy with ML models, it extracts packet features, analyzes patterns, and alerts users with detailed reports for proactive security.