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
翻译:最近,人们有兴趣改进入侵探测系统(IDS)技术的可利用资源,从这个意义上讲,若干与网络安全有关的研究表明,环境入侵和信息绑架日益频繁和复杂,使用计算机资源进行环境业务的关键性使得信息不易受到伤害,网络安全在公司不可或缺的技术领域占据了一个层面,安全小组每天处理防止侵入环境风险的问题,因此,研究的主要目标是利用堆叠方法调查使用堆叠方法的复合学习技术,由支持矢量机和K-Nearest邻居算法提供支持,目的是优化DDoS攻击探测结果。 为此,在应用数据挖掘和机器学习橙子工具以取得更好的结果时,采用了入侵探测系统概念。