Border‐hunting optimization for mobile agent‐based intrusion detection with deep convolutional neural network

Abstract

SummaryThe sensor nodes, which are available in the wireless sensor networks (WSN), are equipped with sensing abilities, and communication. Several domains require the sensor nodes to be arranged in aggressive surroundings, in which observing malicious activities within the sensor network. Therefore, the present research proposes the border‐hunting optimization‐based deep CNN (BHO‐DCNN) for the mobile agent (MA)‐based intrusion detection in WSN. The importance of the research relies on the BHO‐DCNN model for identifying the intrusion available in the network is established by integrating the optimization with its features through a deep classifier for detection in a precise manner. The algorithm follows the communal hierarchy, surrounding, group hunting, and prey attacking, which provides an enhanced rate of convergence in the method of detection. The analysis is achieved through the database IDS 2018 Intrusion CSVs depending on the performance like delay, alive nodes, normalized energy, as well as throughput. The obtained number of alive nodes through the developed BHO‐DCNN algorithm is 45, end‐to‐end delay is 0.2572 ms, normalized energy is 0.1622 J, as well as throughput, is 0.3125% for nodes 50 at the populate rate of 100, respectively.