short-paper

Robust Detection of Network Intrusion using Tree-based Convolutional Neural Networks

Authors:

Rohit Dwivedula,

Varad Kshirsagar,

Chittaranjan HotaAuthors Info & Claims

CODS-COMAD '21: Proceedings of the 3rd ACM India Joint International Conference on Data Science & Management of Data (8th ACM IKDD CODS & 26th COMAD)

Pages 233 - 237

https://doi.org/10.1145/3430984.3431036

Published: 02 January 2021 Publication History

Abstract

Automated Intrusion Detection Systems (IDS) are the first line of defense that monitor network activity to profile and identify suspicious activity. This detection of intrusion is further complicated due to the emergence of sophisticated network based attacks that are difficult to identify. Deep learning approaches have proven to be effective in isolating such attacks through efficient identification of non-linear relationships in data. In this work, we propose a hierarchical Convolutional Neural Network approach, TreeNets, that can be used as an IDS to identify the attacks and segregate them into binary outcomes. The paper depicts the usage of Binary Grey Wolf Optimization approach for identifying the optimal set of features. We exhibit three variants of TreeNets and compare their performance against state of the art machine learning and deep learning models on the NSLKDD dataset. Experimental results depict a competitive performance with an accuracy of 82.16% and 66.37% on KDDTest+ and KDD-Test-21 respectively.

References

[1]

James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl. 2011. Algorithms for Hyper-Parameter Optimization. In Proceedings of the 24th International Conference on Neural Information Processing Systems (Granada, Spain) (NIPS’11). Curran Associates Inc., Red Hook, NY, USA, 2546–2554.

Digital Library

[2]

James Bergstra, Daniel Yamins, and David Cox. 2013. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In International conference on machine learning. JMLR.org, Atlanta, GA, USA, 115–123.

[3]

Eid Emary, Hossam M Zawbaa, and Aboul Ella Hassanien. 2016. Binary grey wolf optimization approaches for feature selection. Neurocomputing 172(2016), 371–381.

Digital Library

[4]

Ruigang Fu, Biao Li, Yinghui Gao, and Ping Wang. 2018. CNN with coarse-to-fine layer for hierarchical classification. IET Computer Vision 12, 6 (2018), 892–899. https://doi.org/10.1049/iet-cvi.2017.0636

Digital Library

[5]

Cosimo Ieracitano, Ahsan Adeel, Francesco Carlo Morabito, and Amir Hussain. 2020. A novel statistical analysis and autoencoder driven intelligent intrusion detection approach. Neurocomputing 387(2020), 51–62.

Digital Library

[6]

Bhupendra Ingre and Anamika Yadav. 2015. Performance analysis of NSL-KDD dataset using ANN. In 2015 international conference on signal processing and communication engineering systems. IEEE, Ottawa, ON, Canada, 92–96. https://doi.org/10.1109/SPACES.2015.7058223

[7]

Yiling Jia, Nipun Batra, Hongning Wang, and Kamin Whitehouse. 2019. A Tree-Structured Neural Network Model for Household Energy Breakdown. In The World Wide Web Conference(WWW ’19). Association for Computing Machinery, New York, NY, USA, 2872–2878. https://doi.org/10.1145/3308558.3313405

Digital Library

[8]

Jie Jiang, Lei Liu, and Guangjun Zhang. 2019. Star Identification Based on Spider-Web Image and Hierarchical CNN. IEEE Trans. Aerospace Electron. Systems 56, 4 (2019), 3055–3062.

[9]

Kaiyuan Jiang, Wenya Wang, Aili Wang, and Haibin Wu. 2020. Network intrusion detection combined hybrid sampling with deep hierarchical network. IEEE Access 8(2020), 32464–32476.

[10]

Thi-Thu-Huong Le, Yongsu Kim, Howon Kim, 2019. Network intrusion detection based on novel feature selection model and various recurrent neural networks. Applied Sciences 9, 7 (2019), 1392.

[11]

Ji-Eun Lee, Min-Joo Kang, and Je-Won Kang. 2017. Ensemble of binary tree structured deep convolutional network for image classification. In 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, Kuala Lumpur, Malaysia, 1448–1451.

[12]

Zhipeng Li, Zheng Qin, Kai Huang, Xiao Yang, and Shuxiong Ye. 2017. Intrusion detection using convolutional neural networks for representation learning. In International Conference on Neural Information Processing. Springer, Cham, 858–866.

Digital Library

[13]

Manuel Lopez-Martin, Belen Carro, and Antonio Sanchez-Esguevillas. 2020. Application of deep reinforcement learning to intrusion detection for supervised problems. Expert Systems with Applications 141 (2020), 112963.

Digital Library

[14]

P Macho, N Kurz, A Ulges, R Brylka, T Gietzen, and U Schwanecke. 2018. Segmenting teeth from volumetric ct data with a hierarchical cnn-based approach. In Computer Graphics and Visual Computing, CGVC 2018(CGVC ’18). Eurographics Association, Goslar, DEU, 109–113. https://doi.org/10.2312/cgvc.20181213

Digital Library

[15]

Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, and Puneet Agarwal. 2015. Long short term memory networks for anomaly detection in time series. In Proceedings, Vol. 89. Presses universitaires de Louvain, 89–94.

[16]

Xuehong Mao, Samer Hijazi, Raúl Casas, Piyush Kaul, Rishi Kumar, and Chris Rowen. 2016. Hierarchical CNN for traffic sign recognition. In 2016 IEEE Intelligent Vehicles Symposium (IV). IEEE, Gothenburg, 130–135.

[17]

Seyedali Mirjalili, Seyed Mohammad Mirjalili, and Andrew Lewis. 2014. Grey wolf optimizer. Advances in engineering software 69 (2014), 46–61.

[18]

Setareh Roshan, Yoan Miche, Anton Akusok, and Amaury Lendasse. 2018. Adaptive and online network intrusion detection system using clustering and extreme learning machines. Journal of the Franklin Institute 355, 4 (2018), 1752–1779.

[19]

Deboleena Roy, Priyadarshini Panda, and Kaushik Roy. 2020. Tree-CNN: A hierarchical Deep Convolutional Neural Network for incremental learning. Neural Networks 121(2020), 148 – 160. https://doi.org/10.1016/j.neunet.2019.09.010

Digital Library

[20]

Kamalakanta Sethi, E. Sai Rupesh, Rahul Kumar, Padmalochan Bera, and Y. Venu Madhav. 2020. A context-aware robust intrusion detection system: a reinforcement learning-based approach. Int. J. Inf. Secur. 19, 6 (Dec 2020), 657–678. https://doi.org/10.1007/s10207-019-00482-7

Digital Library

[21]

Mahbod Tavallaee, Ebrahim Bagheri, Wei Lu, and Ali A Ghorbani. 2009. A detailed analysis of the KDD CUP 99 data set. In 2009 IEEE symposium on computational intelligence for security and defense applications. IEEE, Ottawa, ON, 1–6.

[22]

Zhenhua Wang, Xingxing Wang, and Gang Wang. 2018. Learning fine-grained features via a CNN Tree for Large-scale Classification. Neurocomputing 275(2018), 1231 – 1240. https://doi.org/10.1016/j.neucom.2017.09.061

[23]

Chuanlong Yin, Yuefei Zhu, Jinlong Fei, and Xinzheng He. 2017. A deep learning approach for intrusion detection using recurrent neural networks. Ieee Access 5(2017), 21954–21961.

[24]

Kai Yue, Lei Yang, Ruirui Li, Wei Hu, Fan Zhang, and Wei Li. 2019. TreeUNet: Adaptive Tree convolutional neural networks for subdecimeter aerial image segmentation. ISPRS Journal of Photogrammetry and Remote Sensing 156 (2019), 1 – 13. https://doi.org/10.1016/j.isprsjprs.2019.07.007

[25]

Xiaomei Zhang, Yingying Chen, Bingke Zhu, Jinqiao Wang, Ming Tang, and Hanqing Lu. 2018. Tree Hierarchical CNNs for Object Parsing. In Proceedings - International Conference on Image Processing, ICIP. IEEE, Athens, 1588–1592. https://doi.org/10.1109/ICIP.2018.8451301

Cited By

Mills GAcquah DSowah R(2024)Network Intrusion Detection and Prevention System Using Hybrid Machine Learning with Supervised Ensemble Stacking ModelJournal of Computer Networks and Communications10.1155/2024/57756712024:1Online publication date: 29-Sep-2024
https://doi.org/10.1155/2024/5775671
Kumari TMishra S(2024)Tachyon: Enhancing stacked models using Bayesian optimization for intrusion detection using different sampling approachesEgyptian Informatics Journal10.1016/j.eij.2024.10052027(100520)Online publication date: Sep-2024
https://doi.org/10.1016/j.eij.2024.100520
Kumari TMishra S(2023)Optimized Tree-based Ensembles for Intrusion Detection in Internet of Things2023 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)10.1109/ANTS59832.2023.10469257(1-6)Online publication date: 17-Dec-2023
https://doi.org/10.1109/ANTS59832.2023.10469257
Show More Cited By

Recommendations

Intrusion Detection System for NSL-KDD Dataset Using Convolutional Neural Networks
CSAI '18: Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence

With the increment of cyber traffic, there is a growing demand for cyber security. How to accurately detect cyber intrusions is the hotspot of recent research. Traditional Intrusion Detection Systems (IDS), based on traditional machine learning methods, ...
Network Intrusion Detection: Automated and Manual Methods Prone to Attack and Evasion

In this article, the authors describe common intrusion detection techniques, NIDS evasion methods, and how NIDSs detect intrusions. Additionally, we introduce new evasion methods, present test results for confirming attack outcomes based on server ...
Rule generalisation in intrusion detection systems using SNORT

Intrusion Detection Systems (IDSs) provide an important layer of security for computer systems and networks. An IDS's responsibility is to detect suspicious or unacceptable system and network activity and to alert a systems administrator to this ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

CODS-COMAD '21: Proceedings of the 3rd ACM India Joint International Conference on Data Science & Management of Data (8th ACM IKDD CODS & 26th COMAD)

January 2021

453 pages

ISBN:9781450388177

DOI:10.1145/3430984

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 January 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper
Research
Refereed limited

Funding Sources

TCS PhD Fellowship

Conference

CODS COMAD 2021

CODS COMAD 2021: 8th ACM IKDD CODS and 26th COMAD

January 2 - 4, 2021

Bangalore, India

Acceptance Rates

Overall Acceptance Rate 197 of 680 submissions, 29%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
110
Total Downloads

Downloads (Last 12 months)10
Downloads (Last 6 weeks)0

Reflects downloads up to 18 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Mills GAcquah DSowah R(2024)Network Intrusion Detection and Prevention System Using Hybrid Machine Learning with Supervised Ensemble Stacking ModelJournal of Computer Networks and Communications10.1155/2024/57756712024:1Online publication date: 29-Sep-2024
https://doi.org/10.1155/2024/5775671
Kumari TMishra S(2024)Tachyon: Enhancing stacked models using Bayesian optimization for intrusion detection using different sampling approachesEgyptian Informatics Journal10.1016/j.eij.2024.10052027(100520)Online publication date: Sep-2024
https://doi.org/10.1016/j.eij.2024.100520
Kumari TMishra S(2023)Optimized Tree-based Ensembles for Intrusion Detection in Internet of Things2023 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)10.1109/ANTS59832.2023.10469257(1-6)Online publication date: 17-Dec-2023
https://doi.org/10.1109/ANTS59832.2023.10469257
Kaissar ANassif AInjadat M(2022)A Survey on Network Intrusion Detection using Convolutional Neural NetworkITM Web of Conferences10.1051/itmconf/2022430100343(01003)Online publication date: 14-Mar-2022
https://doi.org/10.1051/itmconf/20224301003
Lansky JAli SMohammadi MMajeed MKarim SRashidi SHosseinzadeh MRahmani A(2021)Deep Learning-Based Intrusion Detection Systems: A Systematic ReviewIEEE Access10.1109/ACCESS.2021.30972479(101574-101599)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3097247
Kaddoura SArid AMoukhtar M(2021)Evaluation of Supervised Machine Learning Algorithms for Multi-class Intrusion Detection SystemsProceedings of the Future Technologies Conference (FTC) 2021, Volume 310.1007/978-3-030-89912-7_1(1-16)Online publication date: 25-Oct-2021
https://doi.org/10.1007/978-3-030-89912-7_1

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents