Abstract: This paper introduces a Machine Learning-based detection system for Denial of Service attacks on WSNs providing robust cybersecurity to these vulnerable systems. The class imbalance problem is quite significant in the WSN-DS dataset, so SMOTE will be used to create synthetic samples to balance the distribution of instances for attack and normal data. Then, feature selection is used which guides the search for relevant attributes to effectively detect attacks. Further, three different machine learning models were trained and evaluated: Logistic Regression, Decision Tree, and REPTree, measuring them in terms of accuracy, precision, recall, and F1-score. This study illustrates that this approach works towards correctly identifying the diverse categories of DoS attacks very efficiently and creates grounds for more effective security strategies for WSNs.
Keywords: Wireless Sensor Networks, Denial of Service, Distributed Denial of Service, Machine Learning.
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DOI:
10.17148/IJARCCE.2025.14444
