Data-Driven Detection of Network Threats Using Advanced Machine Learning Techniques for Cybersecurity

The more sophisticated and diverse the network threats become, the lower the conventional intrusion detection systems' precision and versatility.This work provides a Data Driven Intrusion Detection System (IDS) based on Artificial Neural Networks (ANN) in combination with Principal Component Analysis (PCA) to improve features and minimize dimensionality.A significant amount of preprocessing is performed on the proposed model including missing value handling, normalization and removal of outliers for quality data.The ANN model outperformed the benchmark models Random Forest and Isolation Forest, with 97.5% detection accuracy, 99.0% precision, 96.7% recall, and 95.7% F1-score on the NSL-KDD dataset.These findings also demonstrate that the ANN-based IDS can effectively identify complex and dynamic cyber threats and solve a number of real-world cybersecurity issues.In addition, the model shows strong generalization and efficient learning over validation criteria across dynamic network environments which validates the stability and practicability of the model.