论文标题
入侵检测系统的混合模型
Hybrid Model For Intrusion Detection Systems
论文作者
论文摘要
随着对不断增长的网络流量的新攻击越来越多，立即提醒任何恶意活动以避免损失敏感数据和金钱的挑战。这使入侵检测是网络安全关注的主要领域之一。基于异常的网络入侵检测技术是最常用的技术之一。根据用于测试这些技术的数据集，精度有所不同。在大多数情况下，该数据集并不代表真实的网络流量。考虑到这一点，该项目涉及对入侵检测系统中使用的不同机器学习算法进行分析，当时在两个类似于当前现实世界网络流量（CICIDS2017）的数据集上进行了测试，并改善了KDD 99（NSL-KDD）。在对两个数据集的不同入侵检测系统进行了分析之后，该项目旨在为入侵检测系统开发新的混合模型。这种新的混合动力方法使用堆叠方案结合了决策树和随机森林算法，对于NSL-KDD数据集的精度为85.2％，精度为86.2％，CICIDS2017数据集的精度为98％，精度为98％，精度为98％。
With the increasing number of new attacks on ever growing network traffic, it is becoming challenging to alert immediately any malicious activities to avoid loss of sensitive data and money. This is making intrusion detection as one of the major areas of concern in network security. Anomaly based network intrusion detection technique is one of the most commonly used technique. Depending upon the dataset used to test those techniques, the accuracy varies. Most of the times this dataset does not represent the real network traffic. Considering this, this project involves analysis of different machine learning algorithms used in intrusion detection systems, when tested upon two datasets which are similar to current real world network traffic(CICIDS2017) and an improvement of KDD 99 (NSL-KDD). After the analysis of different intrusion detection systems on both the datasets, this project aimed to develop a new hybrid model for intrusion detection systems. This new hybrid approach combines decision tree and random forest algorithms using stacking scheme to achieve an accuracy of 85.2% and precision of 86.2% for NSL-KDD dataset, and achieve an accuracy of 98% and precision of 98% for CICIDS2017 dataset.
