Enhancing intrusion detection: a hybrid machine and deep learning approach

Muhammad Sajid, Kaleem Razzaq Malik, Ahmad Almogren, Tauqeer Safdar Malik, Ali Haider Khan, Jawad Tanveer, Ateeq Ur Rehman
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Abstract

The volume of data transferred across communication infrastructures has recently increased due to technological advancements in cloud computing, the Internet of Things (IoT), and automobile networks. The network systems transmit diverse and heterogeneous data in dispersed environments as communication technology develops. The communications using these networks and daily interactions depend on network security systems to provide secure and reliable information. On the other hand, attackers have increased their efforts to render systems on networks susceptible. An efficient intrusion detection system is essential since technological advancements embark on new kinds of attacks and security limitations. This paper implements a hybrid model for Intrusion Detection (ID) with Machine Learning (ML) and Deep Learning (DL) techniques to tackle these limitations. The proposed model makes use of Extreme Gradient Boosting (XGBoost) and convolutional neural networks (CNN) for feature extraction and then combines each of these with long short-term memory networks (LSTM) for classification. Four benchmark datasets CIC IDS 2017, UNSW NB15, NSL KDD, and WSN DS were used to train the model for binary and multi-class classification. With the increase in feature dimensions, current intrusion detection systems have trouble identifying new threats due to low test accuracy scores. To narrow down each dataset’s feature space, XGBoost, and CNN feature selection algorithms are used in this work for each separate model. The experimental findings demonstrate a high detection rate and good accuracy with a relatively low False Acceptance Rate (FAR) to prove the usefulness of the proposed hybrid model.
增强入侵检测:一种机器和深度学习混合方法
由于云计算、物联网(IoT)和汽车网络的技术进步,通信基础设施上传输的数据量最近有所增加。随着通信技术的发展,网络系统在分散的环境中传输多样化的异构数据。利用这些网络进行的通信和日常互动都有赖于网络安全系统提供安全可靠的信息。另一方面,攻击者也加大了对网络系统的攻击力度。由于技术进步带来了新型攻击和安全限制,因此高效的入侵检测系统至关重要。本文利用机器学习(ML)和深度学习(DL)技术实现了一种入侵检测(ID)混合模型,以解决这些限制。所提出的模型利用极端梯度提升(XGBoost)和卷积神经网络(CNN)进行特征提取,然后将这两种技术与长短期记忆网络(LSTM)相结合进行分类。四个基准数据集 CIC IDS 2017、UNSW NB15、NSL KDD 和 WSN DS 被用来训练二元和多类分类模型。随着特征维度的增加,当前的入侵检测系统由于测试准确率较低而难以识别新的威胁。为了缩小每个数据集的特征空间,本研究在每个独立模型中使用了 XGBoost 和 CNN 特征选择算法。实验结果表明,混合模型具有较高的检测率和较好的准确性,错误接受率(FAR)相对较低,证明了该模型的实用性。
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