AI-Based Detection of Network Intrusions Using Machine Learning Techniques

This study examined how well machine learning can detect intrusions in modern networks. It compared a Z-score baseline, Isolation Forest, an Autoencoder, and a proposed Hybrid Ensemble. The machine learning methods clearly outperformed the traditional statistical approach in finding anomalous and malicious activity. Despite these promising results, future work could extend the framework in several directions: ●     Real-time intrusion detection: Future work can deploy the proposed models in real-time network monitoring to evaluate their performance on live traffic. ●     Advanced deep learning architectures: Newer models such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Transformer, and Graph Neural Network (GNN) architectures can be explored to improve detection of sophisticated and evolving cyberattacks. ●     Larger and more diverse datasets: Evaluating the framework on larger datasets with more attack types, traffic patterns, and network configurations would improve the generalizability of the results.

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