Efficient Authentication Scheme: AI-Assisted Detection Model for Node Capturing Threats in Cyber Security Applications

A wireless sensor network (WSN) is an enormous collection of inexpensive, self-organizing, specialized sensor nodes with no control. Owing to its constrained resource capacity and reliance on the outside atmosphere to circulate data, WSN is very susceptible to various physical threats. One of the main WSN attacks is called node capture, in which the attacker physically seizes the node and takes the confidential data out of its memory. This study proposed Hybrid Penguin-deer Fusion Algorithm (HPDFA) is built upon a node capturing threats in cyber security method. Its objectives include finding the ideal nodes while minimizing resource expenditures and maximizing node contribution. Together with an effective cost and maximal assault efficiency, it determines the impact of all-inclusive instrument to destroy the largest portion of the network. In comparison to previous methods of node capture attack techniques, the outcome of a simulation indicates that HPDFA receives the highest percentage of hacked traffic, less energy expenditure and fewer attacking rounds. Therefore, by capturing the fewest nodes that expose the entire network, HPDFA provides optimum attacking efficiency while compared to existing algorithms.