Security Risk Intelligent Assessment of Power Distribution Internet of Things via Entropy-Weight Method and Cloud Model

A novel network security situation assessment model based on multiple strategies whale optimization algorithm and bidirectional GRU

The rapid development of the internet has brought about a comprehensive transformation in human life. However, the challenges of cybersecurity are becoming increasingly severe, necessitating the implementation of effective security mechanisms. Cybersecurity situational awareness can effectively assess the network status, facilitating the formulation of better cybersecurity defense strategies. However, due to the low accuracy of existing situational assessment methods, situational assessment remains a challenge. In this study, a new situational assessment method, MSWOA-BiGRU, combining optimization algorithms and temporal neural networks, was proposed. Firstly, a scientific indicator system proposed in this research is used to calculate the values of each indicator. Then, the Analytic Hierarchy Process is used to derive the actual situation values, which serve as labels. Taking into account the temporal nature of network traffic, the BiGRU model is utilized for cybersecurity situational assessment. After integrating time-related features and network traffic characteristics, the situational assessment value is obtained. During the evaluation process, a whale optimization algorithm (MSWOA) improved with a mix of strategies proposed in this study was employed to optimize the model. The performance of the proposed MSWOA-BiGRU model was evaluated on publicly available real network security datasets. Experimental results indicate that compared to traditional optimization algorithms, the optimization performance of MSWOA has seen significant enhancement. Furthermore, MSWOA-BiGRU demonstrates superior performance in cybersecurity situational assessment compared to existing evaluation methods.

Multi-Risk Assessment of Mine Lithium Battery Fire Based on Quantitative Factor Characterization

As a large number of new energy is employed as the driving force for the operation and transportation machinery of underground space projects, the lithium battery load in confined spaces, such as working faces, roadways and tunnels increases in geometric progression, and the coupled risks of heat damage and smoke poisoning caused by possible fires become more serious. In this paper, experimental and numerical methods were implemented to study the propagation mechanism of heat- and mass-induced disasters under catastrophic conditions, and a quantitative characterization model of multiple risk factors of thermal runaway and toxic gas diffusion of battery fire was proposed. The fuzzy analytical hierarchy process (FAHP) was conducted to calculate and grade the risk of lithium battery fire in a typical mine working face under multiple factors, including hazard source, personnel, working environment and emergency response. In addition, a quantitative early warning and control model was established for identified high-risk probability events. The results promote the quantitative and scientific development of multiple risk assessment and decision-making of confined space fire.