ANALISIS STATISTIK LOG JARINGAN UNTUK DETEKSI SERANGAN DDOS BERBASIS NEURAL NETWORK


Arif Wirawan Muhammad(1*); Imam Riadi(2); Sunardi Sunardi(3);

(1) Universitas Ahmad Dahlan Yogyakarta
(2) Universitas Ahmad Dahlan Yogyakarta
(3) Universitas Ahmad Dahlan Yogyakarta
(*) Corresponding Author

  

Abstract


Distributed denial-of-service (DDoS) merupakan jenis serangan dengan volume, intensitas, dan biaya mitigasi yang terus meningkat seiring berkembangnya skala organisasi. Penelitian ini memiliki tujuan untuk mengembangkan sebuah pendekatan baru untuk mendeteksi serangan DDoS, berdasarkan log jaringan yang dianalisis secara statistik dengan fungsi neural network sebagai metode deteksi. Data pelatihan dan pengujian diambil dari CAIDA DDoS Attack 2007 dan simulasi mandiri. Pengujian terhadap metode analisis statistik terhadap log jaringan dengan fungsi neural network sebagai metode deteksi menghasilkan prosentase rata-rata pengenalan terhadap tiga kondisi jaringan (normal, slow DDoS, dan DDoS) sebesar 90,52%. Adanya pendekatan baru dalam mendeteksi serangan DDoS, diharapkan bisa menjadi sebuah komplemen terhadap sistem Intrusion Detection System (IDS) dalam meramalkan terjadinya serangan DDoS.


Keywords


DDoS, Neural Network, Log Jaringan

  
     

Article Metrics

Abstract view: 2157 times
PDF (Bahasa Indonesia) view: 1628 times 		     

Digital Object Identifier

doi  https://doi.org/10.33096/ilkom.v8i3.76.220-225 		  

Cite

How to cite item

References


Arbor Networks, “Worldwide Infrastructure Security Report,” vol. IX, pp. 1–83, 2014.

W. Hurst, N. Shone, and Q. Monnet, “Predicting the Effects of DDoS Attacks on a Network of Critical Infrastructures,” 2015.

T. Ishitaki, D. Elmazi, Y. Liu, T. Oda, L. Barolli, and K. Uchida, “Application of Neural Networks for Intrusion Detection in Tor Networks,” Proc. - IEEE 29th Int. Conf. Adv. Inf. Netw. Appl. Work. WAINA 2015, pp. 67–72, 2015.

J. Wu, X. Wang, X. Lee, and B. Yan, “Detecting DDoS attack towards DNS server using a neural network classifier,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 6354 LNCS, no. PART 3, pp. 118–123, 2010.

E. Balkanli, J. Alves, and A. N. Zincir-Heywood, “Supervised Learning to Detect DDoS Attacks,” IEEE Int. Conf. Adv. Comput. Commun. Informatics, 2014.

A. M. Chandrasekhar and K. Raghuveer, “Intrusion Detection Technique by Using K-Means, Fuzzy Neural Network and SVM Classifiers,” 2013 Int. Conf. Comput. Commun. Informatics, pp. 1–7, 2013.

A. Olabelurin, S. Veluru, A. Healing, and M. Rajarajan, “Entropy Clustering Approach for Improving Forecasting in DDoS Attacks,” 2015.

W. Gautama, Y. Purwanto, and T. W. Purboyo, “Anomali Trafik, DDoS, Flash Crowd, Isodata, Clustering, Manhattan Distance, Dunn Index,” Int. J. Appl. Inf. Technol., pp. 1–8, 2016.

A. Iswardani and I. Riadi, “Denial of Service Log Analysis Using Density K- Means Method,” J. Theor. Appl. Inf. Technol., no. April, 2016.

R. Smith, N. Japkowicz, M. Dondo, and P. Mason, “Using Unsupervised Learning for Network Alert Correlation,” Univ. Ottawa Canada, 2008.

A. Saied, R. E. Overill, and T. Radzik, “Detection of Known and Unknown DDoS Attacks Using Artificial Neural Networks,” J.M. Corchado al. PAAMS 2014 Work., vol. 172, pp. 385–393, 2015.

T. Zhao, D. C. T. Lo, and K. Qian, “A Neural Network Based DDoS Detection System Using Hadoop and HBase,” Proc. - 2015 IEEE 17th Int. Conf. High Perform. Comput. Commun. 2015 IEEE 7th Int. Symp. Cybersp. Saf. Secur. 2015 IEEE 12th Int. Conf. Embed. Softw. Syst. H, pp. 1326–1331, 2015.


Refbacks



Copyright (c) 2016 Arif Wirawan Muhammad

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.