This is a simple DNN based application for an IDS based on the CICIDS2017 dataset

ML based IDS developed based on CICIDS2017 dataset

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With increasing cyber threats, traditional security often falls short. Advanced solutions like ml-based intrusion detection systems (IDS) are vital. The CICIDS2017 dataset, has diverse, realistic attack simulations, is crucial for training model to detect subtle anomalies. Our research enhances IDS accuracy, efficiency, bolstering network defenses.

IDS ML DL with cicids2017 dataset

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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.

Design and evaluation of a scalable offline intrusion detection system using CICIDS2017 dataset with behavioral flow analysis, engineered features, and machine learning classification.

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Adversarial ML on network IDS: feature controllability constraints reduce attack success 35% and enable architectural defenses that outperform adversarial training. CICIDS2017, 5-seed evaluation, govML-governed.

An ML-based IDS to monitor network traffic and detect cyberattacks using a Feedforward Neural Network Trained and evaluated the model on the CICIDS2017 dataset using Python

🛡️ CyberShield IDS — ML-powered Network Intrusion Detection System built with Streamlit. Features real-time packet capture, attack simulation, CICIDS2017 dataset analytics, RandomForest classifier (99.87% accuracy), and Supabase authentication.

Using the CICIDS2017 dataset we are going to create an IDS with different baseline ML model. Then we will introduce FGSM and PGD to measure the impact of Adversarial Attacks.

An Intrusion Detection System (IDS) leveraging Machine Learning to detect network anomalies and potential threats. Built using the CICIDS2017 dataset, this project implements advanced ML techniques for robust and accurate intrusion detection.

Production-ready hybrid IDS using ML + XAI for network security. 2-stage detection (Binary + Multiclass) with PyTorch autoencoder for anomaly detection. 99.56% accuracy on CICIDS2017. FastAPI backend, Streamlit UI, Docker deployment ready.

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.

ntrusion Detection System (IDS) using Machine Learning. Detects cyberattacks (DoS, DDoS, brute force, infiltration, botnet) with NSL-KDD and CICIDS2017 datasets. Uses ML models (SVM, Random Forest, XGBoost), feature selection (RFE, RFECV, Boruta), and anomaly detection (Isolation Forest, LOF), achieving up to 99.9% accuracy.

This repository implements a Network Intrusion Detection System (IDS) using ML techniques and Malware Analysis tool. It includes features such as data analysis with the CICIDS2017 dataset, KNN classifier for attack detection, and static malware analysis capabilities. The interface is built with Streamlit. Docker is used for easy deployment.

AI-based Cybersecurity Simulator: Uses datasets (NSL-KDD, CICIDS2017, custom logs) to train ML models (RF, SVM, DL) for attack detection. Simulates DDoS, phishing, brute-force in Java, predicts attack type, and gives auto responses via GUI. SQLite stores logs. Used in IT, banks, defense, and training for real-time IDS testing.