Journal Article

Enhancing intrusion detection: a hybrid machine and deep learning approach

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

2024

Overview

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.

• Publisher: Springer Berlin Heidelberg,Springer Nature B.V,SpringerOpen
• Subject: Accuracy / Algorithms / Artificial neural networks / Classification / Cloud computing / Communications systems / Computer Communication Networks / Computer Science / Computer System Implementation / Computer Systems Organization and Communication Networks / Cyber security / Cybersecurity / Datasets / Deep learning / Feature extraction / Hybrid systems / Information Systems Applications (incl.Internet) / Internet of Things / Intrusion detection system / Intrusion detection systems / Machine learning / Security systems / Software Engineering/Programming and Operating Systems / Special Purpose and Application-Based Systems