论文标题
有效的基于元疗法的分类器用于多类入侵检测
Effective Metaheuristic Based Classifiers for Multiclass Intrusion Detection
论文作者
论文摘要
由于计算机网络和应用程序的指数增长，网络安全已成为网络安全领域的最大问题。入侵检测在信息系统或网络设备的安全性中起着重要作用。入侵检测系统（IDS）的目的是检测恶意活动，然后对这些活动产生警报。拥有大量数据是检测攻击的关键问题之一。大多数入侵检测系统都使用数据集的所有功能来评估模型，结果是，检测率较低，计算时间和许多计算机资源的用途。对于快速攻击，检测ID需要轻质数据。特征选择方法扮演着关键角色，以选择最佳功能以达到最高精度。这项研究工作进行了实验，考虑了两个更新的攻击数据集，即UNSW-NB15和CICDDOS2019。这项工作表明，基于包装器的遗传算法（GA）具有与集合分类器的选择方法。与现有方法相比，GA选择最佳特征子集并达到高精度，检测率（DR）和低误报率（FAR）。这项研究的重点是多类分类。实施两种合奏方法：堆叠和包装以检测不同类型的攻击。结果表明，通过堆叠集合分类器，GA可以显着提高准确性。
Network security has become the biggest concern in the area of cyber security because of the exponential growth in computer networks and applications. Intrusion detection plays an important role in the security of information systems or networks devices. The purpose of an intrusion detection system (IDS) is to detect malicious activities and then generate an alarm against these activities. Having a large amount of data is one of the key problems in detecting attacks. Most of the intrusion detection systems use all features of datasets to evaluate the models and result in is, low detection rate, high computational time and uses of many computer resources. For fast attacks detection IDS needs a lightweight data. A feature selection method plays a key role to select best features to achieve maximum accuracy. This research work conduct experiments by considering on two updated attacks datasets, UNSW-NB15 and CICDDoS2019. This work suggests a wrapper based Genetic Algorithm (GA) features selection method with ensemble classifiers. GA select the best feature subsets and achieve high accuracy, detection rate (DR) and low false alarm rate (FAR) compared to existing approaches. This research focuses on multi-class classification. Implements two ensemble methods: stacking and bagging to detect different types of attacks. The results show that GA improve the accuracy significantly with stacking ensemble classifier.
