Lightweight Machine Learning for DoS Attack Detection in Wireless Sensor Networks: An Implemented Decision Tree System

Wireless sensor networks require intrusion detection methods that respect limited memory, processing capacity, bandwidth, and battery supply.This paper presents an implemented multiclass Denial-of-Service (DoS) attack detection system for Wireless Sensor Networks using the WSN-DS dataset and lightweight machine-learning models.The public prototype exposes manual prediction and bulk CSV analysis through a React front end and a Flask API.The application reports 374,661 dataset records, 17 input features, five attack classes, and a headline accuracy of 99.47%.Its model-comparison dashboard reports the highest displayed accuracy for a 17-feature Random Forest at 99.72%; however, the Random Forest is not the most time-or memory-efficient option.The study evaluates the full 17-feature representation and also selects a reduced Decision Tree using the five highest ranked features: Is CH, ADV S, SCH S, Data Sent To BS, and Expanded Energy.The five classes are Normal, Flooding, Blackhole, Grayhole, and TDMA.The selected five-feature Decision Tree provides the intended balance of accuracy, training efficiency, model size, interpretability, and reduced feature processing overhead for a lightweight WSN detector.Training-time and memory values are reported as displayed by the dashboard and are not interpreted as packet level inference latency or direct energy measurements.The study links a reproducible web deployment to a resource-aware WSN DoS detection design and identifies the measurements required for hardware-level validation.