Machine learning-based intrusion detection for resource constrained networks

The rapid growth of interconnected devices in small office and home networks has introduced heightened cybersecurity risks, yet traditional Intrusion Detection Systems (IDS) often demand extensive computational resources, making them unsuitable for deployment in resource-constrained environments. This study presents the design, implementation, and evaluation of a lightweight machine learning-based IDS optimized for small networks with limited processing power and memory. The research employed the CICIDS2017 dataset as the primary benchmark, subjecting it to comprehensive preprocessing, including data cleaning, normalization, encoding, feature scaling, and dimensionality reduction through Principal Component Analysis (PCA). Multiple classical Machine Learning algorithms, including Decision Tree, Random Forest (pruned), Naïve Bayes, K-Nearest Neighbors, and Ridge Classifier, were implemented and comparatively evaluated. Performance metrics such as accuracy, precision, recall, F1-score, CPU utilization, memory usage, and latency were used for assessment. Results indicated that the Random Forest achieved the best balance between accuracy and efficiency with low false positive rates, and minimal computational requirements suitable for lightweight environments. The Random Forest was integrated into a Flask-based RESTful API and a Streamlit dashboard. By bridging machine learning techniques with practical deployment frameworks, it contributes a resource-efficient, scalable, and user-friendly security solution tailored to small enterprises and personal network environments.