Year 2021,
Volume: 63 Issue: 1, 17 - 24, 30.06.2021
Ibrahim Ibrahim
,
Sefer Kurnaz
References
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A new distributed denial-of-service detection system in cloud environment by using deep belief networks
Year 2021,
Volume: 63 Issue: 1, 17 - 24, 30.06.2021
Ibrahim Ibrahim
,
Sefer Kurnaz
Abstract
This study presents new method to detect DDOS attacks by using Deep Belief Networks (DBN). The input data which represented the DDoS features in cloud environment are first analyzed by using DBN to extracted high level and sensitive features. The output of the DBN wired to the classifier (SoftMax and SVM). The aim of using the DBN is to extracted features that have ability to present the best classification results and to speed up the processing time by reducing the dimension of features. In the last stage, the Classifier trained in supervised method to classify the features into two labels there is attack or not. The obtained results compared with well-known studies presented in this field.
References
- Mirkovic, J., Reiher, P., A taxonomy of DDoS attack and DDoS defense mechanisms, ACM SIGCOMM Comput. Commun. Rev., 34( 2) (2004), p. 39.
- Wang, D., Yufu, Z., Jie, J., A multi-core based DDoS detection method, Proc. - 2010 3rd IEEE Int. Conf. Comput. Sci. Inf. Technol. ICCSIT 2010, 4 (2010), 115–118.
- Karim, A.M., Kaya, H., Güzel, M.S., Tolun, M.R., Çelebi, F.V., Mishra, A., A Novel Framework Using Deep Auto-Encoders Based Linear Model for Data Classification, Sensors, 20 (2020), 6378.
- Karim, A.M., Serdar, G.M., Tolun, M.R., Kaya, H., Çelebi, F.V., A new framework using deep auto-encoder and energy spectral density for medical waveform data classification and processing, Biocybern. Biomed. Eng., 39 (2019), 148–159.
- Karim, A.M., Karal, Ö., Çelebi, F.V., A New Automatic Epilepsy Serious Detection Method by Using Deep Learning Based on Discrete Wavelet Transform, 4 (2018), 15–18.
- Karim, A.M. Güzel, M.S., Tolun, M.R., Kaya, H., Çelebi, F.V., A New Generalized Deep Learning Framework Combining Sparse Auto-encoder and Taguchi Method for Novel Data Classification and Processing, Volume 2018, Article ID 3145947, (2018), 13 pages.
- Hang, B., Hu, R., Shi, W., An enhanced SYN cookie defense method for TCP DDoS attack, J. Networks, 6(8) (2011),1206–1213.
- Karim, A.M., Çelebi, F.V., Mohammed, A.S., Software Development for Blood Disease Expert System, Lecture Notes on Empirical Software Engineering, 4(3) (2016),179–183.
- Nashat, D., Jiang, X., Horiguchi, S., Router based detection for low-rate agents of DDoS attack, Int. Conf. High Perform. Switch. Routing, HPSR 2008, March (2008), 177-182.
- Huang, M.L., Hung, Y.H. Lee, W.M., Li, R.K., Jiang, B.R., SVM-RFE based feature selection and taguchi parameters optimization for multiclass SVM Classifier, Sci. World J., 2014.
- Zuo, W.M., Lu, W. G., Wang, K.Q., Zhang, H., Diagnosis of cardiac arrhythmia using kernel difference weighted KNN classifier, Comput. Cardiol., 35 (2008), 253–256.
- Yu, Z., et al., Prostatic Schistosoma japonicum with atypical immunophenotyping of individual glandular tubes: a case report and review of the literature, Southeast Asian J. Trop. Med. Public Health, 44(4) (2013), 568–573.