Cybersecurity Intrusion Detection using Machine Learning

Academic PoC using Decision Trees, Random Forest, K-Means and Apriori Algorithms

Kaggle Dataset: https://www.kaggle.com/code/i0nlyaziz/cybersecurity-intrusion-detection

The CyberIntrusionDetectionML repository, is an Academic PoC, that aims to build, evaluate and optimize Machine-Learning Models that can automatically determine whether a network event represents a cyber attack or normal behavior. The entire project revolves around a binary classification target variable:

attack_detected = 0 → No attack (Normal traffic)

attack_detected = 1 → Attack detected (Malicious traffic)

Using this binary label, the repository trains and tests several ML algorithms such as Decision Trees, Random Forests, K-Means clustering, and Apriori rule mining to learn patterns that distinguish safe traffic from harmful activity.