Intrusion Detection based on Correlation of Multiple Health Signals in WBSN

Improvements made in the area of safety and life support, prove the usefulness of smart sensors that collect human physiological data from a remote area using wireless communication. Nevertheless, these techniques do not meet all the Wireless Body Sensor Networks (WBSN) security criteria, which are important for making efficient medical decisions. This needs a suitable intrusion detection technique. This paper proposes a correlation process to enhance medical anomaly detection. The proposed algorithm is based on real physiological signals, which are the Electrocardiogram (ECG), Electromyogram (EMG) and Electro Dermal Activity (EDA). The data analyses are collected from both hardware and software platforms named Open Signals and Bitalino respectively. Furthermore, the novel approach is capable of managing anomalies by introducing an evaluation process based on a correlation algorithm. Simulations show that the novel method is promising for the accurate detection of anomalies.