Abstract
With the astonishing development of the Internet and its applications in the last decade, cyberattacks are changing quickly, and the necessity of protection for communication network has improved tremendously. As the primary defense, the intrusion detection system plays a crucial role in making sure the network security. Key to intrusion detection system is actually to determine a variety of attacks effectively as well as to adjust to a constantly changing threat scenario. DNN or Deep Neural Network on NSL-KDD dataset for effective detection of an attack. Firstly, the dataset was preprocessed and normalized and then fed to the DNN algorithm to create a model. For testing purpose, entire dataset of NSL-KDD was used. Finally, to analyze the accuracy and precision of the DNN model, we use accuracy and precision matrices. The proposed DNN-based strategy enhances network anomaly detection and opens new analysis gateway for intrusion detection systems.
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
References
S. Chung, K. Kim, A heuristic approach to enhance the performance of intrusion detection system using machine learning algorithms, in Proceedings of the Korea Institutes of Information Security and Cryptology Conference (CISC-W’15) (2015)
N. Gao, L. Gao, Q. Gao, H. Wang, An intrusion detection model based on deep belief networks, in 2014 Second International Conference on Advanced Cloud and Big Data (CBD) (2014), pp. 247–252
D. Shin, K. Choi, S. Chune, H. Choi, Malicious traffic detection using K-means. J. Korean Inst. Commun. Inf. Sci. 41(2), 277–284 (2016)
S. Jo, H. Sung, B. Ahn, A comparative study on the performance of SVM and an artificial neural network in intrusion detection. J. Korea Acad.-Ind. Cooperation Soc. 17(2), 703–711 (2016)
P. Laskov, P. Dssel, C. Schfer, K. Rieck, Learning intrusion detection: supervised or unsupervised?’ in Proceedings of the 13th International Conference on Image Analysis and Processing (ICIAP), Cagliari, Italy, ed. by F. Roli, S. Vitulano (Springer, Berlin, 2005), pp. 50–57
A. Solanas, A. Martinez-Balleste, Advances in Artificial Intelligence for Privacy Protection and Security (Intelligent Information Systems) (World Scientific, Hackensack, NJ, 2010) (Online)
D.K. Bhattacharyya, J.K. Kalita, Network Anomaly Detection: A Machine Learning Perspective (CRC Press, Boca Raton, FL, 2013)
M. Tahir, W. Hassan, A. Md Said, N. Zakaria, N. Katuk, N. Kabir, M. Omar, O. Ghazali, N. Yahya, Hybrid machine learning technique for intrusion detection system, in 5th International Conference on Computing and Informatics (ICOCI) (2015)
W. Wang et al., HAST-IDS: learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection. IEEE Access 6, 1792–1806 (2018)
W. McCulloch, W. Pitts, A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5(4), 115–133 (1943)
W. McCulloch, W. Pitts, Results of the KDD’99 classifier learning. ACM SIGKDD Explor. Newsl. 1(2), 63–64 (2000)
O. AI-Jarrah, A. Arafat, Network intrusion detection system using neural network classification of attack behavior. J. Adv. Inf. Technol. 6(1) (2015)
G. Dahl, T. Sainath, G. Hinton, Improving deep neural networks for LVCSR using rectified linear units and dropout, in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (2013), pp. 8609–8613
D. Kingma, J. Ba Adam, A Method for Stochastic Optimization, arXiv preprint arXiv:1412.6980 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Liu, Z. et al. (2020). Deep Learning Approach for IDS. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Fourth International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 1041. Springer, Singapore. https://doi.org/10.1007/978-981-15-0637-6_40
Download citation
DOI: https://doi.org/10.1007/978-981-15-0637-6_40
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0636-9
Online ISBN: 978-981-15-0637-6
eBook Packages: EngineeringEngineering (R0)