论文标题
在网络安全的背景下，用于入侵检测系统的合奏学习技术
Ensemble learning techniques for intrusion detection system in the context of cybersecurity
论文作者
论文摘要
最近，人们有兴趣改善入侵检测系统（IDS）技术中可用的资源。从这个意义上讲，与网络安全有关的一些研究表明，环境入侵和绑架信息越来越复杂且复杂。使用计算资源在环境中涉及运营的业务的关键性不允许信息的脆弱性。网络安全已在公司中必不可少的技术宇宙中占据了一项维度，并且安全团队每天都会处理入侵进入环境的风险。因此，该研究的主要目的是使用支持向量机（SVM）和k-neartible邻居（KNN）算法支持的堆叠方法研究集合学习技术，旨在优化DDOS攻击检测结果的结果。为此，使用数据挖掘和机器学习橙工具的侵入检测系统概念以获得更好的结果
Recently, there has been an interest in improving the resources available in Intrusion Detection System (IDS) techniques. In this sense, several studies related to cybersecurity show that the environment invasions and information kidnapping are increasingly recurrent and complex. The criticality of the business involving operations in an environment using computing resources does not allow the vulnerability of the information. Cybersecurity has taken on a dimension within the universe of indispensable technology in corporations, and the prevention of risks of invasions into the environment is dealt with daily by Security teams. Thus, the main objective of the study was to investigate the Ensemble Learning technique using the Stacking method, supported by the Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) algorithms aiming at an optimization of the results for DDoS attack detection. For this, the Intrusion Detection System concept was used with the application of the Data Mining and Machine Learning Orange tool to obtain better results
