Adaptive and Lightweight Abnormal Node Detection via Biological Immune Game in Mobile Multimedia Networks

Yajing Zhang,Kai Wang,Jinghui Zhang

doi:10.3390/a14120368

Abstract

Considering the contradiction between limited node resources and high detection costs in mobile multimedia networks, an adaptive and lightweight abnormal node detection algorithm based on artificial immunity and game theory is proposed in order to balance the trade-off between network security and detection overhead. The algorithm can adapt to the highly dynamic mobile multimedia networking environment with a large number of heterogeneous nodes and multi-source big data. Specifically, the heterogeneous problem of nodes is solved based on the non-specificity of an immune algorithm. A niche strategy is used to identify dangerous areas, and antibody division generates an antibody library that can be updated online, so as to realize the dynamic detection of the abnormal behavior of nodes. Moreover, the priority of node recovery for abnormal nodes is decided through a game between nodes without causing excessive resource consumption for security detection. The results of comparative experiments show that the proposed algorithm has a relatively high detection rate and a low false-positive rate, can effectively reduce consumption time, and has good level of adaptability under the condition of dynamic nodes.

Highlights

IntroductionIn 1994, Forrest proposed the SNS theory [15], which strictly divides cells and molecules in the immune body into their own cells and allogeneic cell molecules, including foreign viruses, bacteria, and mutated cells
In order to solve the above problems, this paper studies the detection of abnormal edge nodes in a mobile multimedia networking scenario based on artificial immunity theory and game theory
We addressed the problem of node data G

Summary

Introduction

In 1994, Forrest proposed the SNS theory [15], which strictly divides cells and molecules in the immune body into their own cells and allogeneic cell molecules, including foreign viruses, bacteria, and mutated cells. All cell molecules can be defined as a collection U, consisting of a set N of viruses, bacteria, and mutated cells from the outside world, and a set S of their own cells, satisfying. In the SNS mode, antibody cells in the body judge between the cells. According to the above analysis, the SNS theory of self-body and allogeneic cells should satisfy formula (2). Let f be a binary classification function of, and define Ab as an antibody set gained by constantly learning access data in the mechanism of the immune system.

Methods

Results

Conclusion