cyberattack-detection

This project focuses on detecting cyberattacks using advanced analytical techniques and a deep learning model. It leverages structured datasets and explores the use of algorithms for detecting anomalies or malicious behavior in network traffic or system logs.

This project classifies network intrusions using machine learning models. It utilizes the CICIDS2017 dataset to detect various attack types. The model used in this project is implemented in a single Jupyter Notebook file instead of separate scripts.

Phishing is a cyberattack technique in which malicious actors create fraudulent websites that closely resemble legitimate ones to deceive users into revealing sensitive information such as login credentials, financial details, and personal data.

OpenEnv environment for AI-driven cyberattack detection on SRF Phase-Locked Loops in grid-connected inverters. 3 tasks: easy/medium/hard. FastAPI + Docker. HF Space live.