Intrusion-Detection-System-Using-Machine-Learning and Intrusion-Detection-System-Using-CNN-and-Transfer-Learning
These are complementary approaches to the same problem—one uses traditional ML algorithms (decision trees, random forests, XGBoost) while the other uses deep learning (CNN and transfer learning)—so practitioners might evaluate both or combine their predictions for ensemble-based intrusion detection.
About Intrusion-Detection-System-Using-Machine-Learning
Western-OC2-Lab/Intrusion-Detection-System-Using-Machine-Learning
Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
This project helps cybersecurity professionals and network engineers identify cyber-attacks in connected vehicle networks. It takes network traffic data as input and outputs classifications of known or unknown intrusion attempts. The end-user persona is likely a security analyst or an operations engineer responsible for securing Internet of Vehicles (IoV) infrastructure.
About Intrusion-Detection-System-Using-CNN-and-Transfer-Learning
Western-OC2-Lab/Intrusion-Detection-System-Using-CNN-and-Transfer-Learning
Code for intrusion detection system (IDS) development using CNN models and transfer learning
This project helps cybersecurity engineers and automotive security researchers build advanced intrusion detection systems for connected vehicles. It takes network traffic data or vehicle sensor data (like CAN bus data) as input and outputs a highly accurate system capable of identifying cyber-attacks. The primary users are security specialists working with Internet of Vehicles (IoV) systems who need robust defenses against new threats.
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