Research And Application Of Big Data Cybersecurity Situation Awareness Technology: An Improved LSTM Cybersecurity Situation Prediction Model Based On SSA

Abstract

In the current era of rapid global informatization, network coverage has permeated all aspects of life and industry, bringing about significant conveniences while also posing serious cybersecurity challenges. This paper explores the urgent need for an efficient and reliable cybersecurity defense system to address increasingly complex threats, emphasizing the importance of rationalizing perceptions of cybersecurity issues and enhancing security measures. The study focuses on Network Security Situation Awareness (NSSA) technology, which provides strong support for network monitoring through real-time analysis. It highlights the significance of NSSA in promptly identifying risks and predicting security situations to minimize potential threats effectively. By reviewing existing research on situation awareness models, including Endsley’s three-level model and other advanced methodologies, this paper discusses how these frameworks can be applied to improve cybersecurity. Additionally, it examines the integration of big data technology with NSSA to handle massive datasets efficiently. The proposed approach not only enhances the accuracy of threat detection but also supports decision-making processes. Experimental results demonstrate that the SSA-LSTM model outperforms traditional models like LSTM and BP neural networks in terms of prediction accuracy and error rates.