<![CDATA[Rapid growth in local area network (LAN) infrastructure across educational institutions and offices introduces increasingly complex cyber threats. Conventional rule-based Intrusion Detection Systems (IDS) often fail to identify zero-day attacks and exhibit high false alarm rates. This study evaluates the effectiveness of an artificial intelligence-based IDS by comparing three machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB) using the UNSW-NB15 benchmark dataset. The pre-processing pipeline includes data cleaning, Min-Max normalization, categorical encoding, and feature selection via Recursive Feature Elimination (RFE), reducing the feature space from 42 to 15 dominant attributes. Performance was evaluated using Accuracy, Precision, Recall, F1-Score, and Inference Latency per packet. Experimental results demonstrate that Random Forest significantly outperforms other models, achieving 98.2% Accuracy, 97.9% Precision, 98.0% Recall, and a 97.9% F1-Score, with a low inference latency of 0.03 seconds per packet. In comparison, SVM achieved 95.1% Accuracy (0.12s latency), while Naive Bayes reached 89.4% Accuracy with the fastest latency of 0.01s. This research confirms that combining Random Forest with RFE provides an effective and efficient foundation for adaptive IDS deployment in resource-constrained LAN environments.]]>
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