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首页> 外文会议>International Conference in Mathematics, Computer Engineering and Computer Science >Evaluation Of Selected Meta Learning Algorithms For The Prediction Improvement Of Network Intrusion Detection System
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Evaluation Of Selected Meta Learning Algorithms For The Prediction Improvement Of Network Intrusion Detection System

机译：所选元学习算法的评估，用于预测网络入侵检测系统的改进

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摘要

Information security is a critical issue for many organizations, Intrusion detection system (IDS) promises potential solution to analyzing network traffics to detect and alert any attempt to compromise computer systems and its resources. Current penetration methods put previous works on IDS to a challenge. Stacked ensemble improves IDS prediction accuracy. This research focus on the Evaluation of selected Meta Learner algorithms for IDS improvement. Base-level IDS models of K Nearest Neighbour, Naive Bayes’ and Decision Tree predictions were used to trained three selected meta learners’ algorithms;(Meta Decision Tree (MDT), Multi Response Linear Regression (MLR) and Multiple Model Trees (MMT)). The evaluations of base-level and meta stacked models using UNSWNB15 test data show that, MDT models recorded the highest intrusion detection accuracy, closely followed by MMT meta learner stacked ensemble models, MLR recorded the least classification accuracy. MDT stacked ensemble models recorded the least misclassification rate, followed by MMT stacked ensemble and MLR recorded the highest misclassification rate. All the three Meta learners’ models recorded better intrusion detection accuracy than the best accuracy recorded by each of the base-level model.
机译：信息安全是许多组织的关键问题，入侵检测系统（IDS）承诺潜在的解决方案来分析网络流量，以检测和警告任何损害计算机系统及其资源的尝试。目前的渗透方法将以前的职位造成挑战。堆叠的集合可以提高IDS预测准确性。本研究重点是对IDS改进所选元学习者算法的评估。 K最近邻居，天真贝叶斯和决策树预测的基础IDS模型用于训练三个选定的元学习者算法;（元决策树（MDT），多响应线性回归（MLR）和多个模型树（MMT） ）。使用UNSWNB15测试数据的基础和元堆叠模型的评估表明，MDT模型记录了最高入侵检测精度，紧随其后的是MMT元学习者堆叠集合模型，MLR记录了最小的分类准确性。 MDT堆叠集合模型记录了最低错误分类率，其次是MMT堆叠集合，MLR记录了最高的错误分类率。所有三个元学习者的模型都记录了比每个基础级模型记录的最佳精度更好的入侵检测精度。

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