Abstract
Advances in communication and networking technology leads to the use of internet-based technology in Industrial Control System (ICS) applications. Simultaneously to the advantages and flexibility, it also opens doors to the attackers. Increased attacks on ICS are clear examples for the need of developing strong security mechanisms to develop defense in depth strategies for industries. Despite several techniques, every day a novel attack is being identified and this highlights the importance and need of robust techniques for identifying those attacks. Deep learning-based intrusion detection mechanisms are proven to be efficient in identifying novel attacks. Deep learning techniques such as Stacked Autoencoders (SAE), Deep Belief Networks (DBN) are widely used for intrusion detection but the research on using Convolutional Neural Networks (CNN) is limited. In this paper, the efficiency of CNN based intrusion detection for identifying the multiple attack classes using datasets such as NSL-KDD and UNSW-NB 15 is evaluated. Different performance metrics such as precision, recall and F-measure were calculated and compared with the existing deep learning approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Stallings, W.: Network security essentials : applications and standards (2000)
Scarfone, K., Mell, P.: Guide to intrusion detection and prevention systems (IDPS) recommendations of the National Institute of Standards and Technology. NIST Spec. Publ. 800–94, 127 (2007)
Tofino Security, SCADA Security Basics: Why Industrial Networks are Different than IT Networks (2012). https://www.tofinosecurity.com/blog/scada-security-basics-why-industrial-networks-are-different-it-networks
Colbert, E.J.M., Kott, A. (eds.): Cyber-security of SCADA and Other Industrial Control Systems. AIS, vol. 66. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32125-7
M. Lincoln Laboratory, DARPA Intrusion Detection Data Sets. https://www.ll.mit.edu/ideval/data/. Accessed 07 Apr 2016
Sahu, S.K., Sarangi, S., Jena, S.K.: A detail analysis on intrusion detection datasets. In: Souvenir 2014 IEEE International Advance Computing Conference (IACC 2014), May, pp. 1348–1353 (2014)
McHugh, J.: Testing intrusion detection systems: a critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln Laboratory. ACM Trans. Inf. Syst. Secur. 3(4), 262–294 (2000)
Revathi, D.A.M.S.: A detailed analysis on NSL-KDD dataset using various machine learning techniques for intrusion detection. Int. J. Eng. Res. Technol. 2(12), 1848–1853 (2013)
Tang, T.A., Mhamdi, L., McLernon, D., Zaidi, S.A.R., Ghogho, M.: Deep learning approach for network intrusion detection in software defined networking. In: 2016 International Conference on Wireless Networks and Mobile Communications, pp. 258–263 (2016)
Javaid, A., Niyaz, Q., Sun, W., Alam, M.: A deep learning approach for network intrusion detection system. In: Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS) (2016)
Sheikhan, M., Jadidi, Z., Farrokhi, A.: Intrusion detection using reduced-size RNN based on feature grouping. Neural Comput. Appl. 21(6), 1185–1190 (2012)
Yin, C., Zhu, Y., Fei, J., He, X.: A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access 5(c), 21954–21961 (2017)
Alom, Z., Bontupalli, V., Taha, T.M.: Intrusion detection using deep belief networks, pp. 339–344 (2015)
Potluri, S., Diedrich, C.: Deep feature extraction for multi-class intrusion detection in industrial control systems. Int. J. Comput. Theory Eng. 9(5), 374–379 (2017)
Potluri, S., Diedrich, C.: Accelerated deep neural networks for enhanced Intrusion Detection System. In: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2016 (2016)
Potluri, S., Henry, N.F., Diedrich, C.: Evaluation of hybrid deep learning techniques for ensuring security in networked control systems. In: 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation, pp. 1–8 (2017)
Shone, N., Ngoc, T.N., Phai, V.D., Shi, Q.: A deep learning approach to network intrusion detection. IEEE Trans. Emerg. Top. Comput. Intell. 2(1), 41–50 (2018)
Li, Z., Qin, Z., Huang, K., Yang, X., Ye, S.: Intrusion detection using convolutional neural networks for representation learning. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.-S.M. (eds.) ICONIP 2017. LNCS, vol. 10638, pp. 858–866. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70139-4_87
Vinayakumar, R., Soman, K.P., Poornachandran, P.: Applying convolutional neural network for network intrusion detection. In: 2017 International Conference on Advanced Computing, Communications and Informatics, pp. 1222–1228 (2017)
Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set. In: IEEE Symposium on Computational Intelligence in Security and Defense Applications, CISDA 2009, pp. 1–6 (2009)
Dhanabal, L., Shantharajah, S.P.: A study on NSL-KDD dataset for intrusion detection system based on classification algorithms. Int. J. Adv. Res. Comput. Commun. Eng. 4, 446–452 (2015)
Moustafa, N., Slay, J.: UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In: 2015 Military Communications and Information Systems Conference, pp. 1–6 (2015)
Moustafa, N., Slay, J.: The evaluation of network anomaly detection systems: statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set. Inf. Secur. J. 25(1–3), 18–31 (2016)
Moustafa, N., Slay, J.: The UNSW-NB15 data set description (2015). https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/. Accessed 06 Apr 2018
Wu, J.: Introduction to Convolutional Neural Networks, pp. 1–28 (2016)
Agarap, A.F.: Deep Learning using Rectified Linear Units (ReLU), no. 1 (2018)
Wu, H., Gu, X.: Max-pooling dropout for regularization of convolutional neural networks. In: Arik, S., Huang, T., Lai, W.K., Liu, Q. (eds.) ICONIP 2015. LNCS, vol. 9489, pp. 46–54. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26532-2_6
Karn, U.: An intuitive explanation of convolutional neural networks. The Data Science Blog (2016). https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/. Accessed 06 May 2018
Bhandare, A., Bhide, M., Gokhale, P., Chandavarkar, R.: Applications of convolutional neural networks. Int. J. Comput. Sci. Inf. Technol. 7(5), 2206–2215 (2016)
MathWorks, Deep Learning Basics, Documentation (2018). https://www.mathworks.com/help/nnet/deep-learning-basics.html. Accessed 06 May 2018
MathWorks, Real-Time UDP (2018). https://www.mathworks.com/help/xpc/real-time-udp.html. Accessed 06 May 2018
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Potluri, S., Ahmed, S., Diedrich, C. (2018). Convolutional Neural Networks for Multi-class Intrusion Detection System. In: Groza, A., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2018. Lecture Notes in Computer Science(), vol 11308. Springer, Cham. https://doi.org/10.1007/978-3-030-05918-7_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-05918-7_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05917-0
Online ISBN: 978-3-030-05918-7
eBook Packages: Computer ScienceComputer Science (R0)