As these attacks become more and more difficult to see, the need for the great hi-tech models that detect them is undeniable. This paper examines and compares various machine learning as well as deep learning models to choose the most suitable ones for detecting and fighting against cybersecurity risks. The two datasets are used in the study to assess models like Naive Bayes, SVM, Random Forest, and deep learning architectures, i.e., VGG16, in the context of accuracy, precision, recall, and F1-score. Analysis shows that Random Forest and Extra Trees do better in terms of accuracy though in different aspects of the dataset characteristics and types of threat. This research not only emphasizes the strengths and weaknesses of each predictive model but also addresses the difficulties associated with deploying such technologies in the real-world environment, such as data dependency and computational demands. The research findings are targeted at cybersecurity professionals to help them select appropriate predictive models and configure them to strengthen the security measures against cyber threats completely.

翻译：随着网络攻击日益隐蔽难测，构建高效检测模型的需求愈发迫切。本文系统比较了多种机器学习与深度学习模型，旨在筛选出最适合网络安全威胁检测与防御的算法。研究采用两个独立数据集，评估了朴素贝叶斯、支持向量机、随机森林等传统机器学习模型以及VGG16等深度学习架构在准确率、精确率、召回率和F1分数等指标上的表现。分析表明，随机森林与极端随机树模型在整体准确率上表现更优，但其性能优势随数据集特征与威胁类型的变化而呈现差异。本研究不仅揭示了各预测模型的优缺点，还探讨了实际部署中面临的数据依赖性与计算资源需求等挑战。研究成果可为网络安全从业者提供模型选择与配置的参考依据，助力构建更完备的网络安全防御体系。