Network Intrusion Detection-Project

Implemented a Network Intrusion Detection System (NIDS) using machine learning algorithms and network traffic analysis. The project involved the development of anomaly detection models to identify and alert on potential security threats and unauthorized activities within a network. Utilized datasets containing both normal and malicious network traffic patterns, applying supervised learning techniques for accurate threat detection. The NIDS enhances cybersecurity measures by providing real-time monitoring and proactive response capabilities.

Tools used :- Python, Scikit-learn, Pandas, Matplotlib, Random Forests, Support Vector Machines (SVM)

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