cicids

Real-time Intrusion Detection System implementing Machine Learning. We combine Supervised Learning (RF) for detecting known attacks from CICIDS 2018 & SCVIC-APT datasets, and Unsupervised Learning (AE) for anomaly detection.

Network data classifier based on the recurrent neural network.

Deep Model Intrusion Detection (IDS) Evaluation of NSL KDD and CIC IDS 2018 datasets.

This project aims to identify and classify the anomalies captured in network traffic using different machine learning strategies. After the reults are given, I compared the results of two classical approaches for supervised learning: RandomForest and SVM on a large public combined dataset made from CICIDS2017 dataset and CICIDS2018.

DNN-Ensemble IDS is a machine learning based classification model for intrusion detection exploiting ensembles of classifiers.

Empirical comparison of FedAvg, FedProx, and FedNova for network intrusion detection under pathological non-IID partitioning on CIC-IDS-2017.

Machine learning-based network intrusion detection system using Random Forest, FastAPI, and Streamlit.

Real-time Network Intrusion Detection System using Random Forest ML + Flask REST API + React dashboard with live packet capture via Scapy