Hybrid attacks collaborative defense model using an ensemble honey badger algorithm

Abstract

Crowdsensing networks can be attacked by multiple malicious attacks at the same time, which seriously affects the reliability and security of the network. However, the existing defense technology mainly defends against specific types of attacks and cannot meet the security requirements of hybrid malicious attacks. Based on this, a hybrid attack collaborative defense model based on the integrated honey badger algorithm is proposed. First, the Crowdsensing network is formally described, and the collaborative mechanism of users participating is generalized. Through defense skill matching, efficient collaboration between users is achieved to meet the defense requirements of hybrid attacks. Then, the integrated honey badger algorithm is designed to solve the collaborative defense multi-objective optimization problem. The search intensity between multiple objectives is balanced by individual fitness evaluation, and the advantages and disadvantages of individuals in the collaborative defense scheme on the defense objectives are evaluated, and the Pareto optimal defense scheme is selected. At the same time, the algorithm continuously updates the collaborative defense scheme by selecting the exploration method suitable for the current defense state, and iteratively obtains the global optimal collaborative defense scheme. Finally, hybrid attack simulation and collaborative defense method performance analysis are carried out in the shared bicycle scheduling scenario. The experimental results show the feasibility and effectiveness of the proposed collaborative defense method.