使用非负矩阵分解和单变量分析进行入侵检测系统的有效特征提取
Effective Feature Extraction for Intrusion Detection System using Non-negative Matrix Factorization and Univariate analysis
AI总结：
提出结合非负矩阵分解和卡方分析的分层特征提取方法，降低过拟合风险与噪声，在NSL-KDD和CICD 2017数据集上分别提升4.66%和0.39%性能。
摘要：
入侵检测系统（IDS）对于避免恶意活动至关重要。通常，IDS通过机器学习方法得到改进，但由于数据包（每条记录中包含过多头部或特征，模型效率正在下降。提出的模型使用非负矩阵分解和卡方分析提取实用特征。特征数量过多会增加模型指数级时间和过拟合风险。结合这两种技术，提出的模型构建了一种分层方法，可减少特征二次误差和噪声。该模型在三个公开数据集上实现，显示出显著改进。根据最新研究，该模型在NSL-KDD和CICD 2017上分别提升了4.66%和0.39%的性能。
英文摘要：
An Intrusion detection system (IDS) is essential for avoiding malicious activity. Mostly, IDS will be improved by machine learning approaches, but the model efficiency is degrading because of more headers (or features) present in the packet (each record). The proposed model extracts practical features using Non-negative matrix factorization and chi-square analysis. The more number of features increases the exponential time and risk of overfitting the model. Using both techniques, the proposed model makes a hierarchical approach that will reduce the features quadratic error and noise. The proposed model is implemented on three publicly available datasets, which gives significant improvement. According to recent research, the proposed model has improved performance by 4.66% and 0.39% with respective NSL-KDD and CICD 2017.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
