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There is need to provide resilient security methodologies that do not require enormous computing resources. While entry prevention is the most viable disposition, it is not always possible to stop unauthorised access. Thus, it is critical to investigate the use of machine learning-based intrusion detection to buttress and provide sufficient security against DOS and other attacks in MANETs. Various anomaly-based intrusion detection systems employ varying techniques to identify anomalies in the context of diverse and valid variables. Most of these techniques, however, fail to capture and take account the physiognomies of MANETs. In the intervening time, usage of the internet of things in the provision of smart healthcare is expanding and the inherent risks snowballing. This study designed a model, which used a fusion of machine learning techniques through both simulation and a running prototype to achieve a more resilient intrusion detection system. The study was designed using functional decomposition methodology and implemented using PPDIO and evaluated on a MANET environment on both Linux NS 2 and further implemented on a network of Smart wearable devices and Raspberry Pi.

Designing a Fused Machine Learning Model for the Provision of Smart Health Care in MANETS