Abstract:
In the present era, the number of Internet of Health Things (IoHT) devices and applications has drastically expanded.
Security and attack are major issues in the IoHT domain because of the nature of its architecture and sorts of devices. Over the recent
few years, network attacks have dramatically increased. Many detection and encryption techniques are there in the literature, but
they lack accuracy, training stability, insecurity, delay etc. By the above concerns, this manuscript introduces a novel deep learning
technique called Agnostic Spiking Binarized neural network with Improved Billiards optimization for accurate detection of network
attacks and Light Weight integrated Puzzle War Elliptic Curve Cryptographic framework for secure data transmission with high
security and minimal delay. Optimal features from the datasets are selected by volcano eruption optimization algorithm with better
convergence for reducing the overall processing time. Wilcoxon Rank Sum and Mc Neymar’s tests are performed for proving the
statistical analyses. The outcomes show that the introduced approach performs with an overall accuracy of 99.99% which is better
than the previous techniques demonstrating the effectiveness.
