Tayyab Khan

With an increasing reliance on mobile devices, continuous and assured user authentication is essential to protect sensitive personal data and digital interactions from unwanted access. Based on this background, this research proposed the development of the HybridTouch framework for smartphone-based continuous and passive user authentication. The proposed HybridTouch combines Convolutional Neural Networks for spatial feature extraction and Gated Recurrent Units for temporal sequence analysis. It uses accelerometer, gyroscope, and touch data to take advantage of the unique behavioral patterns captured by it. Innovative preprocessing techniques have been incorporated into the proposed approach: Discrete Wavelet Transform is used for signal denoising, and Variable-Length Adaptive Temporal windowing is used for segmentation based on signal entropy to enhance feature representation. To eliminate the data scarcity limitation, Generative Adversarial Networks were used to synthesize realistic behavioral data that considerably augmented the dataset and enhanced model generalization capability. Extensive experiments conducted on the Hand Movement, Orientation, and Grasp (HMOG) dataset showed that the proposed HybridTouch achieved excellent results with authentication accuracy up to 98.8% with real data, growing up to 99% with GAN-augmented data. The hybrid model further has an equal error rate of 1.4% on real data and 1.25% on synthetic data, which is better than any other models currently present (Sağbas et al., 2024; Siddiqui et al., 2022; Abuhamad et al., 2020) and all implementations of standalone convolutional neural networks and gated recurrent units.

Trust establishment (TE) among sensor nodes has become a vital requirement to improve security, reliability, and successful cooperation. Existing trust management approaches for large scale WSN are failed due to their low cooperation (i.e., dependability), higher communication and memory overheads (i.e., resource inefficient). This paper provides a new and comprehensive hybrid trust estimation approach for large scale WSN employing clustering to improve cooperation, trustworthiness, and security by detecting selfish sensor nodes with reduced resource (memory, power) consumption. The proposed scheme consists of unique features like authentication based data trust, scheduler based node trust, and attack resistant by giving the high penalty and minimum reward during node misbehavior. A task scheduling mechanism is employed for scheduling the significant task to reduce computation overhead. The proposed trust model would be capable to provide security against blackhole attack, grey hole attack, and badmouthing attack. Moreover, the proposed trust model feasibility has been tested with MATLAB. Simulation results exhibit the great performance of our proposed approach in terms of trust evaluation cost, prevention, and detection of malicious nodes with the help of analyzing consistency in trust values and communication overhead.

In the era of internet technologies, to provide wireless communication and transfer the information in seconds from one place to another has arrived because of the need to consume information technologies. All users desire to quickly access the smart world’s life and interact with the entire world socially. This paper proposed an environment for the safe and secure smart patient’s room connected to the WSN, BAN, and RFID. All the data will be transferred to the session key, secure and contains the patient’s information. The network connected through WSN and data will be sent through the session key to make an smart hospital’s patient cabin. The small token is there that will be transferred throughout the network to get authenticated by each network. This proposed scheme is secure enough to overcome the drawbacks of the other protocol in such a way as to make the protocol more secure from the entire adversary’s attack may occur.