摘 要
随着网络技术的快速发展，网络恶意行为日益复杂化和隐蔽化，对网络安全构成了严重威胁。为有效应对这一挑战，本研究聚焦于网络恶意行为检测中的特征工程与模型优化问题，旨在通过改进特征提取方法和优化机器学习模型性能，提升检测系统的准确性和效率。研究首先分析了现有检测方法在高维度数据处理和噪声干扰方面的不足，并提出了一种基于自适应特征选择的优化算法，该算法能够动态调整特征权重，显著降低冗余特征对模型性能的影响。同时，引入深度学习框架以增强模型对复杂模式的学习能力，并结合迁移学习策略解决小样本场景下的过拟合问题。实验结果表明，所提出的特征工程方法可将检测精度提升约15%，而优化后的模型在保持较高召回率的同时，误报率降低了近10%。
关键词：网络恶意行为检测 特征工程 自适应特征选择
Abstract
With the rapid development of network technology, network malicious behavior is increasingly complicated and hidden, which poses a serious threat to network security. In order to effectively address this challenge, this study focuses on feature engineering and model optimization in network malicious behavior detection, aiming to improve the accuracy and efficiency of the detection system by improving the feature extraction method and optimizing the performance of the machine learning model. The paper first analyzes the shortcomings of existing detection methods in high-dimensional data processing and noise interference, and proposes an optimization algorithm based on adaptive feature selection, which can dynamically adjust feature weights and significantly reduce the impact of redundant features on model performance. At the same time, the deep learning fr amework is introduced to enhance the ability of the model to learn complex patterns, and the transfer learning strategy is combined to solve the overfitting problem in small sample scenarios. The experimental results show that the proposed feature engineering method can improve the detection accuracy by about 15%, while the optimized model reduces the false positive rate by nearly 10% while maintaining a high recall rate.
Keyword:Network Malicious Behavior Detection Feature Engineering Adaptive Feature Selection
目 录
1绪论 1
1.1网络恶意行为检测的研究背景与意义 1
1.2特征工程与模型优化的国内外研究现状 1
1.3本文研究方法与技术路线 2
2特征工程在恶意行为检测中的应用 2
2.1恶意行为数据的特征提取方法 2
2.2特征选择对检测性能的影响分析 3
2.3高维特征空间的降维技术研究 3
2.4特征工程优化的实际案例探讨 3
3模型优化在恶意行为检测中的实现 4
3.1常见机器学习模型在恶意行为检测中的表现 4
3.2模型参数调优的技术与策略 5
3.3深度学习模型在恶意行为检测中的优势与挑战 5
3.4模型集成方法对检测精度的提升作用 6
4特征工程与模型优化的综合研究 6
4.1特征工程与模型优化的协同关系分析 6
4.2数据不平衡问题的解决方案研究 7
4.3实时检测系统中特征与模型的适配性研究 7
4.4特征工程与模型优化的实际部署与验证 8
结论 8
参考文献 10
致谢 11
