A novel IoT network intrusion detection approach based on Adaptive Particle Swarm Optimization Convolutional Neural Network

In the field of network security, it is of great significance to accurately detect various types of Internet of Things (IoT) network intrusion attacks which launched by the attacker controlled zombie hosts. In this paper, we propose a novel IoT network intrusion detection approach based on Adaptive Particle Swarm Optimization Convolutional Neural Network (APSO-CNN). In particular, the PSO algorithm with change of inertia weight is used to adaptively optimize the structure parameters of one-dimensional CNN. The cross-entropy loss function value of the validation set, which is obtained from the first training of CNN, is taken as the fitness value of PSO. Especially, we define a new evaluation method that considers both the prediction probability assigned to each category and prediction label to compare the proposed APSO-CNN algorithm with CNN set parameters manually (R CNN). Meanwhile, the comprehensive performance of proposed APSO-CNN and other three well known algorithms are compared in the five traditional evaluation indicators and the accuracy statistical characteristics of 10 times independent experiments. The simulation results show that the multi-type IoT network intrusion attack detection task based on APSO-CNN algorithm is effective and reliable. (c) 2021 Elsevier Inc. All rights reserved.