Research Article
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Year 2021, Volume: 63 Issue: 1, 17 - 24, 30.06.2021
https://doi.org/10.33769/aupse.697067

Abstract

References

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  • 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.
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  • 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.
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  • 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.
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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
https://doi.org/10.33769/aupse.697067

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.
There are 12 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Ibrahim Ibrahim 0000-0002-6946-2030

Sefer Kurnaz 0000-0002-6946-2030

Publication Date June 30, 2021
Submission Date March 2, 2020
Acceptance Date June 11, 2020
Published in Issue Year 2021 Volume: 63 Issue: 1

Cite

APA Ibrahim, I., & Kurnaz, S. (2021). A new distributed denial-of-service detection system in cloud environment by using deep belief networks. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, 63(1), 17-24. https://doi.org/10.33769/aupse.697067
AMA Ibrahim I, Kurnaz S. A new distributed denial-of-service detection system in cloud environment by using deep belief networks. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. June 2021;63(1):17-24. doi:10.33769/aupse.697067
Chicago Ibrahim, Ibrahim, and Sefer Kurnaz. “A New Distributed Denial-of-Service Detection System in Cloud Environment by Using Deep Belief Networks”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 63, no. 1 (June 2021): 17-24. https://doi.org/10.33769/aupse.697067.
EndNote Ibrahim I, Kurnaz S (June 1, 2021) A new distributed denial-of-service detection system in cloud environment by using deep belief networks. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 63 1 17–24.
IEEE I. Ibrahim and S. Kurnaz, “A new distributed denial-of-service detection system in cloud environment by using deep belief networks”, Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng., vol. 63, no. 1, pp. 17–24, 2021, doi: 10.33769/aupse.697067.
ISNAD Ibrahim, Ibrahim - Kurnaz, Sefer. “A New Distributed Denial-of-Service Detection System in Cloud Environment by Using Deep Belief Networks”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 63/1 (June 2021), 17-24. https://doi.org/10.33769/aupse.697067.
JAMA Ibrahim I, Kurnaz S. A new distributed denial-of-service detection system in cloud environment by using deep belief networks. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 2021;63:17–24.
MLA Ibrahim, Ibrahim and Sefer Kurnaz. “A New Distributed Denial-of-Service Detection System in Cloud Environment by Using Deep Belief Networks”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, vol. 63, no. 1, 2021, pp. 17-24, doi:10.33769/aupse.697067.
Vancouver Ibrahim I, Kurnaz S. A new distributed denial-of-service detection system in cloud environment by using deep belief networks. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 2021;63(1):17-24.

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering

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