The practise of recognising unauthorised abnormal actions on computer systems is referred to as intrusion detection. The primary goal of an Intrusion Detection System (IDS) is to identify user behaviours as normal or abnormal based on the data they communicate. Firewalls, data encryption, and authentication techniques were all employed in traditional security systems. Current intrusion scenarios, on the other hand, are very complex and capable of readily breaching the security measures provided by previous protection systems. However, current intrusion scenarios are highly sophisticated and are capable of easily breaking the security mechanisms imposed by the traditional protection systems. Detecting intrusions is a challenging aspect especially in networked environments, as the system designed for such a scenario should be able to handle the huge volume and velocity associated with the domain. This research presents three models, APID (Adaptive Parallelized Intrusion Detection), HBM (Heterogeneous Bagging Model) and MLDN (Multi Layered Deep learning Network) that can be used for fast and efficient detection of intrusions in networked environments. The deep learning model has been constructed using the Keras library. The training data is preprocessed and segregated to fit the processing architecture of neural networks. The network is constructed with multiple layers and the other required parameters for the network are set in accordance with the input data. The trained model is validated using the validation data that has been specifically segregated for this purpose.

Emerging Internet of Things technology plays the major role in modern healthcare not only for sensing but also in recording, communication and display results. The major role of an intensive care unit (ICU) is to improve patient health such as bringing about a change in the treatment or move the patient to a step-down unit etc. Monitoring also shows the extent of observance with a formulated standard of care. In ICU, care should be taken to monitor medical parameters, such as EEG, EMG, BP etc , continuously. In recent health care applications such as real time human health condition monitoring, patient information management etc, IoT technology brings convenience of general practitioner and human, since it is applied in various medical areas, the Body Sensor Network (BSN) is one of the main technology of IoT based medical applications, where a tiny smart and lightweight wireless sensor nodes are used for monitoring patient’s health condition. Hence, this paper proposes BSN integrated with IoT based sensor fusion algorithm to save human life those who are in critical condition. Sensor fusion algorithm is used to detect the criticality of the patient’s health condition and IoT technology is used for communicating information. The testbed has been developed using Rasberry Pi controller, EMG sensor,, BP sensor etc and tested. The tested results also analyzed.

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