Abstract Health monitoring using Body Area Wireless Sensor Network (BA-WSN) has gained immense popularity due to usability, ubiquitous support, and real-time performance. It is a special kind of Wireless Sensor Network (WSN) that spans over the human body. Although BA-WSN is very useful but it may suffer from security and privacy issues due to compromised sensor nodes by intruders. To design a secure BA-WSN based health monitoring system, it is required to filter out malicious data packets generated by the compromised nodes. An intrusion Detection System (IDS) can be utilized for this purpose. This paper presents a Machine Learning based Intrusion Detection System (MLIDS) for BA-WSN based health monitoring framework. Specialized dataset WSN-DS has been used to train the intrusion detection model. Dataset contains four security attacks such as Blackhole attack, Grayhole attack, Scheduling attack, Flooding attack data as well as normal data packets which are simulated using Network Simulator-2. Five well-known classification algorithms such as Random Forest, kNN, SVM, J48, and Naive Bayes have been applied for the selection and generation of the best model in terms of detection accuracy. Experimental results prove that Random Forest based Intrusion Detection Model has the highest classification accuracy of 99.67%, 98.7%, 92.7%, 98.9%, 99.9% for Blackhole attack, Flooding attack, Scheduling attack, Grayhole attack as well normal packet respectively. Experimental results also show that our achieved results outperform relevant work in terms of accuracy.
展开▼
