HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

Muhammad Ashfaq Khan

doi:10.3390/pr9050834

journal article Open Access May 10, 2021

HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

Muhammad Ashfaq Khan

Processes Vol. 9 No. 5 pp. 834 · MDPI AG

View at Publisher Save 10.3390/pr9050834

Abstract

Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a very advanced security solution. Thus, creating an effective and smart ID system is a massive research problem. Various ID datasets are publicly available for ID research. Due to the complex nature of malicious attacks with a constantly changing attack detection mechanism, publicly existing ID datasets must be modified systematically on a regular basis. So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based hybrid ID framework that predicts and classifies malicious cyberattacks in the network. In the HCRNNIDS, the convolutional neural network (CNN) performs convolution to capture local features, and the recurrent neural network (RNN) captures temporal features to improve the ID system’s performance and prediction. To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic CSE-CIC-DS2018 data. The simulation outcomes prove that the proposed HCRNNIDS substantially outperforms current ID methodologies, attaining a high malicious attack detection rate accuracy of up to 97.75% for CSE-CIC-IDS2018 data with 10-fold cross-validation.

Topics

No keywords indexed for this article. Browse by subject →

References

69

[1]

Anderson, J.P. (1980). Technical Report. Computer Security Threat Monitoring and Surveillance, James P. Anderson Company.

[2]

Asharf, J., Moustafa, N., Khurshid, H., Debie, E., Haider, W., and Wahab, A. (2020). A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions. Electronics, 9. 10.3390/electronics9071177

[3]

Xu "An Intrusion Detection System Using a Deep Neural Network with Gated Recurrent Units" IEEE Access (2018) 10.1109/access.2018.2867564

[4]

Vinayakumar, R., Soman, K.P., and Poornachandran, P. (2017). Applying convolutional neural network for network intrusion detection. Proceedings of the 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 13–16 September 2017, Institute of Electrical and Electronics Engineers (IEEE). 10.1109/icacci.2017.8126009

[5]

Khan "Deep Learning-Based Hybrid Intelligent Intrusion Detection System" Comput. Mater. Contin. (2021)

[6]

Devi, B.T., Thirumaleshwari, S.S., and Jabbar, M.A. (2020). An Appraisal over Intrusion Detection Systems in Cloud Computing Security Attacks. Proceedings of the 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Bangalore, India, 5–7 March 2020, Institute of Electrical and Electronics Engineers (IEEE). 10.1109/icimia48430.2020.9074924

[7]

Thaseen, I.S., Poorva, B., and Ushasree, P.S. (2020). Network Intrusion Detection using Machine Learning Techniques. Proceedings of the 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), Tamil Nadu, India, 24–25 February 2020, Institute of Electrical and Electronics Engineers (IEEE).

[8]

Yin "A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks" IEEE Access (2017) 10.1109/access.2017.2762418

[9]

Yong, B., Wei, W., Li, K.C., Shen, J., Zhou, Q., Wozniak, M., Połap, D., and Damaševičius, R. (2020). Ensemble machine learning approaches for web shell detection in Internet of things environments. Transactions on Emerging Telecommunications Technologies, John Wiley & Sons, Ltd. 10.1002/ett.4085

[10]

Folino "On learning effective ensembles of deep neural networks for intrusion detection" Inf. Fusion (2021) 10.1016/j.inffus.2021.02.007

[11]

Tama "Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation" Comput. Sci. Rev. (2021) 10.1016/j.cosrev.2020.100357

[12]

Kim, K., Aminanto, M.E., and Tanuwidjaja, H.C. (2018). Network Intrusion Detection Using Deep Learning: A Feature Learning Approach, Springer. 10.1007/978-981-13-1444-5

[13]

Avci "A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications" Mech. Syst. Signal Process. (2021) 10.1016/j.ymssp.2020.107077

[14]

Kumar "Intrusion detection system based on GA-fuzzy classifier for detecting malicious attacks" Concurr. Comput. Pr. Exp. (2021) 10.1002/cpe.5242

[15]

Zhang "An effective convolutional neural network based on SMOTE and Gaussian mixture model for intrusion detection in imbalanced dataset" Comput. Netw. (2020) 10.1016/j.comnet.2020.107315

[16]

Binbusayyis "Identifying and Benchmarking Key Features for Cyber Intrusion Detection: An Ensemble Approach" IEEE Access (2019) 10.1109/access.2019.2929487

[17]

Bhavani, T.T., Rao, M.K., and Reddy, A.M. (2016, January 1–3). Network Intrusion Detection System Using Random Forest and Decision Tree Machine Learning Techniques. Proceedings of the Distributed Computing and Artificial Intelligence, 13th International Conference, Sevilla, Spain.

[18]

Karatas "Increasing the Performance of Machine Learning-Based IDSs on an Imbalanced and Up-to-Date Dataset" IEEE Access (2020) 10.1109/access.2020.2973219

[19]

Xu, H., Przystupa, K., Fang, C., Marciniak, A., Kochan, O., and Beshley, M. (2020). A Combination Strategy of Feature Selection Based on an Integrated Optimization Algorithm and Weighted K-Nearest Neighbor to Improve the Performance of Network Intrusion Detection. Electronics, 9. 10.3390/electronics9081206

[20]

Bhati "Analysis of Support Vector Machine-based Intrusion Detection Techniques" Arab. J. Sci. Eng. (2019) 10.1007/s13369-019-03970-z

[21]

Thaseen "An integrated intrusion detection system using correlation-based attribute selection and artificial neural network" Trans. Emerg. Telecommun. Technol. (2021) 10.1002/ett.4014

[22]

Waskle, S., Parashar, L., and Singh, U. (2020). Intrusion Detection System Using PCA with Random Forest Approach. Proceedings of the 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India, 2–4 July 2020, Institute of Electrical and Electronics Engineers (IEEE). 10.1109/icesc48915.2020.9155656

[23]

Alqahtani "Cyber Intrusion Detection Using Machine Learning Classification Techniques" Communications in Computer and Information Science (2020) 10.1007/978-981-15-6648-6_10

[24]

Ahmad "Network intrusion detection system: A systematic study of machine learning and deep learning approaches" Trans. Emerg. Telecommun. Technol. (2021) 10.1002/ett.4150

[25]

Girdler "Implementing an intrusion detection and prevention system using Software-Defined Networking: Defending against ARP spoofing attacks and Blacklisted MAC Addresses" Comput. Electr. Eng. (2021) 10.1016/j.compeleceng.2021.106990

[26]

Aldweesh "Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues" Knowl. Based Syst. (2020) 10.1016/j.knosys.2019.105124

[27]

Jihyun, K., Jaehyun, K., Huong, L.T.T., and Howon, K. (2016). Long short-term memory recurrent neural network classifier for intrusion detection. Proceedings of the 2016 International Conference on Platform Technology and Service (PlatCon), Jeju, Korea, 15–17 February 2016, IEEE.

[28]

Vinayakumar, R., Soman, K.P., and Poornachandran, P. (2017). Deep android malware detection and classification. Proceedings of the 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 13–16 September 2017, Institute of Electrical and Electronics Engineers (IEEE). 10.1109/icacci.2017.8126084

[29]

Adebowale, M.A., Lwin, K.T., and Hossain, M.A. (2020). Intelligent phishing detection scheme using deep learning algorithms. J. Enterp. Inf. Manag., 1–20.

[30]

Tran "A LSTM based framework for handling multiclass imbalance in DGA botnet detection" Neurocomputing (2018) 10.1016/j.neucom.2017.11.018

[31]

Oliveira, N., Praça, I., Maia, E., and Sousa, O. (2021). Intelligent Cyber Attack Detection and Classification for Network-Based Intrusion Detection Systems. Appl. Sci., 11. 10.3390/app11041674

[32]

Ahmad "Machine learning approaches to IoT security: A systematic literature review" Internet Things (2021) 10.1016/j.iot.2021.100365

[33]

Makuvaza "Deep Neural Network (DNN) Solution for Real-time Detection of Distributed Denial of Service (DDoS) Attacks in Software Defined Networks (SDNs)" SN Comput. Sci. (2021) 10.1007/s42979-021-00467-1

[34]

Millar "Multi-view deep learning for zero-day Android malware detection" J. Inf. Secur. Appl. (2021)

[35]

Guijuan "A survey of autoencoder-based recommender systems" Front. Comput Sci. (2020) 10.1007/s11704-018-8052-6

[36]

Liu "Semi-supervised anomaly detection with dual prototypes autoencoder for industrial surface inspection" Opt. Lasers Eng. (2021) 10.1016/j.optlaseng.2020.106324

[37]

Yousefi-Azar, M., Varadharajan, V., Hamey, L., and Tupakula, U. (2017). Autoencoder-based feature learning for cybersecurity applications. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA, 14–19 May 2017, IEEE. 10.1109/ijcnn.2017.7966342

[38]

Khan, M.A., and Kim, J. (2020). Toward Developing Efficient Conv-AE-Based Intrusion Detection System Using Heterogeneous Dataset. Electronics, 9. 10.3390/electronics9111771

[39]

Yadigar "Deep learning method for denial-of-service attack detection based on restricted Boltzmann machine" Big Data (2018) 10.1089/big.2018.0023

[40]

Tan "Detection of Denial-of-Service Attacks Based on Computer Vision Techniques" IEEE Trans. Comput. (2014) 10.1109/tc.2014.2375218

[41]

Ingre, B., and Yadav, A. (2015, January 2–3). Performance analysis of NSL-KDD dataset using ANN. Proceedings of the 2015 International Conference on Signal Processing and Communication Engineering Systems, Guntur, India. 10.1109/spaces.2015.7058223

[42]

Casas "Unsupervised Network Intrusion Detection Systems: Detecting the Unknown without Knowledge" Comput. Commun. (2012) 10.1016/j.comcom.2012.01.016

[43]

Ludwig, S.A. (2017). Intrusion detection of multiple attack classes using a deep neural net ensemble. Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, USA, 27 November–1 December 2017, Institute of Electrical and Electronics Engineers (IEEE). 10.1109/ssci.2017.8280825

[44]

Shone "A Deep Learning Approach to Network Intrusion Detection" IEEE Trans. Emerg. Top. Comput. Intell. (2018) 10.1109/tetci.2017.2772792

[45]

Kakavand "Effective Dimensionality Reduction of Payload-Based Anomaly Detection in TMAD Model for HTTP Payload" KSII Trans. Internet Inf. Syst. (2016)

[46]

Yu "Network Intrusion Detection through Stacking Dilated Convolutional Autoencoders" Secur. Commun. Netw. (2017) 10.1155/2017/4184196

[47]

Kumar "Design of an Evolutionary Approach for Intrusion Detection" Sci. World J. (2013) 10.1155/2013/962185

[48]

Akyol "Design of Multilevel Hybrid Classifier with Variant Feature Sets for Intrusion Detection System" IEICE Trans. Inf. Syst. (2016) 10.1587/transinf.2015edp7357

[49]

Almomani, O. (2020). A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms. Symmetry, 12. 10.3390/sym12061046

[50]

Monshizadeh "Performance Evaluation of a Combined Anomaly Detection Platform" IEEE Access (2019) 10.1109/access.2019.2930832

Showing 50 of 69 references

Cited By

260

Deep Learning Applications for Intrusion Detection in Network Traffic

A. I. Getman, D. A. Rybolovlev · 2024

Programming and Computer Software

CNN-GRU-FF: a double-layer feature fusion-based network intrusion detection system using convolutional neural network and gated recurrent units

Yakubu Imrana, Yanping Xiang · 2024

Complex & Intelligent Systems

Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction

Md. Alamin Talukder, Md. Manowarul Islam · 2024

Journal of Big Data

A Network Intrusion Detection System Using Hybrid Multilayer Deep Learning Model

Muhammad Basit Umair, Zeshan Iqbal · 2024

Big Data

Modeling an intrusion detection using recurrent neural networks

Mariam Ibrahim, Ruba Elhafiz · 2023

Journal of Engineering Research

Network intrusion detection based on the temporal convolutional model

Ivandro O. Lopes, Deqing Zou · 2023

Computers & Security

Comparative research on network intrusion detection methods based on machine learning

Chunying Zhang, Donghao Jia · 2022

Computers & Security

A Survey of CNN-Based Network Intrusion Detection

Leila Mohammadpour, Teck Chaw Ling · 2022

Applied Sciences

DRaNN_PSO: A deep random neural network with particle swarm optimization for intrusion detection in the industrial internet of things

Jawad Ahmad, Syed Aziz Shah · 2022

Journal of King Saud University - C...

Network Intrusion Detection Model Based on CNN and GRU

Bo Cao, Chenghai Li · 2022

Applied Sciences

Deep Learning-Based Intrusion Detection Systems: A Systematic Review

Jan Lansky, Saqib Ali · 2021

IEEE Access

IDS-attention: an efficient algorithm for intrusion detection systems using attention mechanism

FatimaEzzahra Laghrissi, Samira Douzi · 2021

Journal of Big Data

Metrics

260

Citations

69

References

Details

Published: May 10, 2021
Vol/Issue: 9(5)
Pages: 834
License: View

Authors

M

Muhammad Ashfaq Khan

IoT and Big-Data Research Center, Department of Electronics Engineering, Incheon National University, Incheon 2012, Korea

Cite This Article

Muhammad Ashfaq Khan (2021). HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System. Processes, 9(5), 834. https://doi.org/10.3390/pr9050834

HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

You May Also Like