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New hybrid method for attack detection using combination of evolutionary algorithms, SVM, and ANN

机译：使用进化算法，SVM和ANN组合的攻击检测新的混合方法

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摘要

Intrusion detection systems (IDS) have been playing an important role for providing security of computer networks. They detect different types of attacks and malicious software usage, which sometimes cannot be identified by firewalls. Based on machine learning algorithms, many IDS have been extended to classify network traffic as normal or abnormal. This paper describes a new hybrid intrusion detection method with two phases - a feature selection phase and an attack detection phase. In the feature selection phase, a wrapper technique, namely MGA-SVM, is used. This technique combines features of support vector machine (SVM) and the genetic algorithm with multi-parent crossover and multi-parent mutation (MGA). In the attack detection phase, an artificial neural network (ANN) is used to detect attacks. For improving its performance, a combination of a hybrid gravitational search (HGS) and a particle swarm optimization (PSO) is used to train the classifier. The proposed hybrid method is thus called MGA-SVM-HGS-PSO-ANN. It's performance is compared with other popular techniques such as Chi-SVM, ANN based on gradient descent (GD-ANN) and decision tree (DT), ANN based on genetic algorithm (GA-ANN), ANN based on combining gravitational search (GS) and PSO (GSPSO-ANN), ANN based on PSO (PSO-ANN), and ANN based on GS (GS-ANN). Using the NSL-KDD dataset as a standard benchmark for attack detection evaluation, the obtained test results show that the proposed MGA-SVM-HGS-PSO-ANN method can attain a maximum detection accuracy of 99.3%, dimension reduction of NSL-KDD from 42 to 4 features, and needs only 3 s as maximum training time.
机译：入侵检测系统（IDS）一直在为提供计算机网络的安全性发挥重要作用。他们检测到不同类型的攻击和恶意软件使用情况，这有时无法通过防火墙识别。基于机器学习算法，已经扩展了许多ID以将网络流量分类为正常或异常。本文介绍了一种具有两个阶段的新的混合入侵检测方法 - 特征选择阶段和攻击检测阶段。在特征选择阶段，使用包装器技术，即MGA-SVM。该技术结合了支持向量机（SVM）的特征和多父交叉和多父突变（MGA）的遗传算法。在攻击检测阶段，使用人工神经网络（ANN）来检测攻击。为了提高其性能，混合重力搜索（HGS）和粒子群优化（PSO）的组合用于训练分类器。因此，所提出的杂化方法称为MGA-SVM-HGS-PSO-ANN。它的性能与基于组合引力搜索的遗传算法（GA-ANN），基于基于遗传算法（GA-ANN），基于组合的引力搜索（GS）（GS）（GS）的性能与Chi-SVM，基于梯度下降（GD-ANN），ANN等其他流行技术进行比较基于GS（GS-ANN）的PSO（PSO-ANN）和ANN的PSO（GSPSO-ANN），ANN（GSPSO-ANN）和ANN。使用NSL-KDD数据集作为攻击检测评估的标准基准，所获得的测试结果表明，所提出的MGA-SVM-HGS-PSO-ANN方法可以获得99.3％的最大检测精度，来自的NSL-KDD的尺寸减少42至4个功能，仅需要3秒作为最大培训时间。

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