Abstract
An intrusion detection system is continuous observation of system or over the network assessment of an intruder or any other attacks. In this paper, design, and analysis of intrusion detection system via neuro-fuzzy, neural network and SVM technique for the improvement misuse detection system. The proposed approachable to enhancement anomaly detection and improve these techniques for anomaly detection.
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
Kumar, I., Virmani, J., Bhadauria, H.S.: A review of breast density classification methods. In: Proceeding of 2nd International Conference on Computing for Sustainable Global Development INDIACom, pp. 1960–1967 (2015)
Kumar, I., Virmani, J., Bhadauria, H.S.: Wavelet packet texture descriptors based four-class BIRADS breast tissue density classification. Procedia Comput. Sci. 70, 76–84 (2015)
Kumar, I., Bhadauria, H.S., Virmani, J., Thakur, S.: A classification framework for prediction of breast density using an ensemble of neural network classifiers. Biocybern. Biomed. Eng. 37, 217–228 (2017)
Sabahi, F.: Intrusion detection: a survey. In: The Third International Conference on Systems and Networks Communications. IEEE Computer Society (2008)
Chandrasekhar, A.M. et al.: Intrusion detection technique by using k-means, fuzzy neural network and SVM classifiers. In: IEEEÂ Xplore (2013). https://doi.org/10.1109/iccci201.6466310
Le, T.-H. et al.: An effective intrusion detection classifier using long short-term memory with gradient descent optimization. In: IEEEÂ Xplore (2017). https://doi.org/10.1109/platcon.2017.7883684
Cannady, J.: Artificial neural networks for misuse detection. In: National Information Systems Security Conference (2006)
Vladimir, V.N.: The Nature of Statistical Learning Theory. Springer, Berlin, Heidelberg, New York (2005)
Tiwari, A. et al.: An effective approach for secure video watermarking based on H.264 standard. In: 3rd IEEE International Conference on Computational Intelligence and Communication Technology IEEE Xplorer (2017). 978-1-5090-6218-8/17/$31.00
Tiwari, A., Kamlesh K.Gupta, An effective approach of digital image watermarking for copyright protection. Int. J. Big Data Secur. Intell. 2(1), 7–17 (2015). http://dx.doi.org/10.14257/ijbdsi.2015.2.1.02, ISSN: 2383-7047SERSC
Kumar, I., Bhadauria, H.S., Virmani, J., Thakur, S.A.: A hybrid hierarchical framework for classification of breast density using digitized film screen mammograms Multimedia Tools and Applications pp. 1–25 (2017)
Deng, H., Zeng, Q., Agrawal, D.P.: SVM-based intrusion detection system for wireless ad hoc networks. In Proceedings of Vehicular Technology Conference, pp. 2147–2151 (2003)
Denning, D.: An intrusion-detection model. IEEE Trans. Softw. Eng. SE-13(2) (2016)
Abhishek Tiwari, Neelesh Kumar Jain and Devraj Tomar, Analysis of multiscale transform based digital image watermarking for multimedia files, Int. J. Sci. Res. Devel. ISSN: 2321-0613 vol 2( 2) pp. 177–182 (2014)
Tiwari, A.: Real time intrusion detection system using computational intelligence and neural network: review, analysis and anticipated solution of machine learning. Springer Book Series: Information Technology and Applied Mathematics (2017). ISBN: 978-981-10-7590-2, ISSN 2194-5357
Yan, H. et al.: ANN-based multi classifier for identification of perimeter events. In: IEEEÂ Xplore (2011). https://doi.org/10.1109/iscid.2011.141
Malhotra, S. et al.: Genetic programming and K-nearest neighbour classifier based intrusion detection model. In: IEEEÂ Xplore, ISBN (2017). https://doi.org/10.1109/confluence.2017.7943121
Ghosh, A.K.: Learning program behavior profiles for intrusion detection. In: USENIX (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Tiwari, A., Ojha, S.K. (2019). Design and Analysis of Intrusion Detection System via Neural Network, SVM, and Neuro-Fuzzy. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 755. Springer, Singapore. https://doi.org/10.1007/978-981-13-1951-8_6
Download citation
DOI: https://doi.org/10.1007/978-981-13-1951-8_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1950-1
Online ISBN: 978-981-13-1951-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)