This research paper aims to examine the applicability of predictive analytics to improve the real-time identification and response to cyber-attacks. Today, threats in cyberspace have evolved to a level where conventional methods of defense are usually inadequate. This paper highlights the significance of predictive analytics and demonstrates its potential in enhancing cyber security frameworks. This research integrates literature on using big data analytics for predictive analytics in cyber security, noting that such systems could outperform conventional methods in identifying advanced cyber threats. This review can be used as a framework for future research on predictive models and the possibilities of implementing them into the cyber security frameworks. The study uses quantitative research, using a dataset from Kaggle with 2000 instances of network traffic and security events. Logistic regression and cluster analysis were used to analyze the data, with statistical tests conducted using SPSS. The findings show that predictive analytics enhance the vigilance of threats and response time. This paper advocates for predictive analytics as an essential component for developing preventative cyber security strategies, improving threat identification, and aiding decision-making processes. The practical implications and potential real-world applications of the findings are also discussed.

翻译：本研究旨在探讨预测分析在改进网络攻击实时识别与响应方面的适用性。当前网络空间威胁已发展到传统防御方法通常难以应对的程度。本文强调预测分析的重要性，并论证其在增强网络安全框架方面的潜力。本研究整合了关于利用大数据分析进行网络安全预测分析的文献，指出此类系统在识别高级网络威胁方面可能优于传统方法。本综述可为未来关于预测模型及其在网络安全框架中实施可能性的研究提供框架。本研究采用定量研究方法，使用来自Kaggle的数据集，包含2000个网络流量与安全事件实例。通过逻辑回归和聚类分析处理数据，并运用SPSS进行统计检验。研究结果表明，预测分析能有效提升威胁预警能力与响应速度。本文主张将预测分析作为制定预防性网络安全策略、优化威胁识别并辅助决策过程的核心组成部分。文中同时讨论了研究结果的实际意义与潜在现实应用。