A Deep learning approach for trust-untrust nodes classification problem in WBAN

The enormous growth in demand for WBAN services has resulted in a new set of security challenges. The capabilities of WBAN are developing to meet these needs. The complexity, heterogeneity, and instability of the mobile context make it difficult to complete these duties successfully. A more secure and flexible WBAN setting can be attained using a trust-untrust nodes classification, which is one method to satisfy the security needs of the WBAN. Considering this, we present a novel Deep Learning (DL) approach for classifying WBAN nodes using spatial attention based iterative DBN (SA-IDBN). Z-score normalization is used to remove repetitive entries from the input data. Then, Linear Discriminate Analysis (LDA) is employed to retrieve the features from the normalized data. In terms of accuracy, latency, recall, and f-measure, the suggested method's performance is examined and contrasted with some other current approaches. Regarding the classification of WBAN nodes, the results are more favorable for the suggested method than for the ones already in use

Reactive protocols for unified user profiling for anomaly detection in mobile Ad Hoc networks

The Next Generation mobile network expected to be fully automated to meet the growing need for data rates and quality in communication. These prodigious demands have also increased the amount of data being handled in these wireless networks. The cellular networks can leverage vital data about the user and the network conditions providing all-inclusive visibility and intelligence in communication. Emerging analytic technologies such as big data and neural networks have been used to unearth vital insight from network traffic to assist intelligent models in routing packets. Reactive protocols are an emerging model in the intelligent routing of traffic in ad-hoc networks. In this paper, we first utilize the reactive protocols to route traffic in a wireless network while analyzing anomalous behavior. In the case of anomaly detection in wireless communication, combined performance indicators to identify outliers. The detected outliers been compared with the ground data and routes created using the reactive protocols. The combination of reactive protocols and the key performance indicators in network performance uncovered anomalies leading to segregation of these traffic in routing. From the results, it is evident that an abrupt surge in the traffic indicated an anomaly and identify the areas of interest in a network especially for resource and path allocation and fault avoidance. A MATLAB GUI was used to simulate the reactive protocols for routing of traffic and generation of data sets that analyze in Microsoft Excel to characterize the key performance indicators of the network

Efficient time-series forecasting of nuclear reactions using swarm intelligence algorithms

In this research paper, we focused on the developing a secure and efficient time-series forecasting of nuclear reactions using swarm intelligence (SI) algorithm. Nuclear radioactive management and efficient time series for casting of nuclear reactions is a problem to be addressed if nuclear power is to deliver a major part of our energy consumption. This problem explains how SI processing techniques can be used to automate accurate nuclear reaction forecasting. The goal of the study was to use swarm analysis to understand patterns and reactions in the dataset while forecasting nuclear reactions using swarm intelligence. The results obtained by training the SI algorithm for longer periods of time for predicting the efficient time series events of nuclear reactions with 94.58 percent accuracy, which is higher than the deep convolution neural networks (DCNNs) 93% accuracy for all predictions, such as the number of active reactions, to see how the results can improve. Our earliest research focused on determining the best settings and preprocessing for working with a certain nuclear reaction, such as fusion and fusion task: forecasting the time series as the reactions took 0-500 ticks being trained on 300 epoch