Information Security Risk Assessment Based on Markov Chain Optimization SPA Model

Abstract

As a transformation of libraries combined with digital technology, digital libraries inevitably face risks such as hacker attacks and virus invasions. In order to reduce losses to users and platforms from information leakage, Information security risk assessment of digital libraries becomes an important topic. The traditional method of risk assessment always has a blind spot when assessing dynamic risk factors. Therefore, this paper constructs a fusion model based on Markov Chain optimized Set Pair Analysis model, which makes full use of the powerful function of Markov Chain and Set Pair Analysis in dealing with dynamic model with fuzzy factor. Experimental results show that when tested on the dataset, the model achieves an identification accuracy of 98.4%, an area under the curve of 0.95, and an F1 score of 0.98. Compared with other models, it not only has higher accuracy but also lower false alarm rates. These results indicate that the proposed risk assessment model is applicable and accurate in risk evaluation. It effectively addresses the difficulty of traditional methods in handling dynamic risk factors. The innovation of the model proposed in the research lies in the construction of a risk assessment framework that combines dynamic and static elements. Although the traditional set pair analysis method can handle the static correlations of risk factors, it is difficult to capture their dynamic evolution. The research achieved dynamic modeling and prediction of the risk situation through the state transition mechanism of Markov chains, and introduced the rough set theory to objectively allocate weights for risk indicators, overcoming the limitation of traditional methods where weights rely on subjective experience. Through Bayesian optimization and accelerated gradient strategies for adaptive parameter adjustment, the convergence speed and generalization ability of the model in complex risk environments were significantly improved. This framework not only realizes the deep integration of multiple algorithms at the functional level, but also achieves the unification of dynamics, objectivity and adaptability in the risk assessment of digital library information security, providing new methodological support for the analysis of high-dimensional and time-varying risks. The model proposed in the research, although performing well in terms of performance, involves integrating multiple algorithms, which leads to high computational complexity and high requirements for hardware resources and real-time performance. In practical deployment, it may encounter challenges in adapting to the high heterogeneity of digital library systems and the inconsistent data formats, and in the future, it needs to be further optimized in aspects such as lightweight deployment, cross-platform adaptation, and weak supervision learning.