Title: Security defence technology for webcast integrating SSA and reinforcement learning

Authors: Delu Wang

Addresses: School of Economics and Management, Guangzhou City Construction College, Guangzhou, 510925, China

Abstract: This paper first introduces logistic chaotic mapping and random walk strategy to optimise traditional sparrow search algorithms, and combines them with support vector machines for intrusion detection. Subsequently, reinforcement learning and game model were integrated. The data prove that the loss function of the proposed detection method is the smallest and approaches to 10-6 infinitely when the iteration is 61 times. In the comparison of comprehensive F1 values for detection and defence, when the running time is 0.475 seconds, the F1 value of the proposed method is the highest, reaching 98.31%. In the analysis of defence success rates for different attack strategies, the proposed strategy can achieve a maximum of 99.78% against password intrusion in network live streaming, and can maintain 99.99% against security vulnerabilities in network live streaming security intrusion. This indicates that the proposed security defence technology has implemented various types of network live streaming security intrusion prevention.

Keywords: sparrow search algorithm; SSA; reinforcement learning; online live streaming; defence; intrusion detection.

DOI: 10.1504/IJCSYSE.2026.151335

International Journal of Computational Systems Engineering, 2026 Vol.10 No.1/2/3/4, pp.27 - 35

Received: 03 Aug 2023
Accepted: 05 Sep 2023
Published online: 26 Jan 2026 *

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