Metode Deteksi Intrusi Menggunakan Algoritme Extreme Learning Machine dengan Correlation-based Feature Selection

Penulis

  • Sulandri Sulandri Universitas Brawijaya
  • Achmad Basuki Universitas Brawijaya
  • Fitra Abdurrachman Bachtiar Universitas Brawijaya

DOI:

https://doi.org/10.25126/jtiik.0813358

Abstrak

Deteksi intrusi pada jaringan komputer merupakan kegiatan yang sangat penting dilakukan untuk menjaga keamanan data dan informasi. Deteksi intrusi merupakan proses monitor traffic pada sebuah jaringan untuk mendeteksi adanya pola data yang dianggap mencurigakan, yang memungkinkan terjadinya serangan jaringan. Penelitian ini melakukan analisis pada traffic jaringan untuk mengetahui apakah paket tersebut mengandung intrusi atau merupakan paket normal. Data traffic yang digunakan untuk deteksi intrusi pada penelitian ini diambil dari dataset KDD Cup. Metode yang digunakan untuk melakukan deteksi intrusi dengan cara klasifikasi yaitu dengan menggunakan metode Extreme Learning Machine (ELM). Namun, dengan menggunakan metode ELM saja tidak mampu untuk menghasilkan akurasi yang baik maka, pada metode ELM perlu ditambahkan metode seleksi fitur Correlation-Based Feature Selection (CFS) untuk meningkatkan hasil akurasi dan waktu komputasi. Hasil penelitian yang dilakukan dengan menggunakan metode ELM menunjukkan tingkat akurasi mencapai 81,97% dengan waktu komputasi 3,39 detik. Setelah ditambahkan metode seleksi fitur CFS pada ELM tingkat akurasi meningkat secara signifikan menjadi 98,00% dengan waktu komputasi 2,32 detik.

 

Abstract

Intrusion detection of computer networks is a very important activity carried out to maintain data and information security. Intrusion detection is the process of monitoring traffic on a network to detect any data patterns that are considered suspicious, which allows network attacks. This research analyzes the network traffic to find out whether the packet contains intrusion or is a normal packet. Traffic data used for intrusion detection in this study were taken from the KDD Cup dataset. The method used to do intrusion detection by classification is using the Extreme Learning Machine (ELM) method. However, using the ELM method alone is not able to produce good accuracy, so the ELM method needs to be added to the Correlation-Based Feature Selection (CFS) feature selection method to improve the accuracy and computational time. The results of the research conducted using the ELM method showed an accuracy rate of 81.97% with a computation time of 3.39 seconds. After adding the CFS feature selection method to ELM the accuracy level increased significantly to 98.00% with a computing time of 2.32 seconds.

Downloads

Download data is not yet available.

Referensi

ABBAS, M., ALBADR, A. & TIUN, S. 2017 ‘Extreme Learning Machine: A Review’, 12(14), pp. 4610–4623.

AHMAD, I. & ISKANDAR, B. S. 2009 ‘Application of Artificial Neural Network in Detection of Probing Attacks’, 2009 IEEE Symposium on Industrial Electronics & Applications. IEEE, 2(Isiea), pp. 557–562. doi: 10.1109 /ISIEA.2009. 5356382.

BENOÎT, F. dkk. 2013 ‘Feature Selection for Nonlinear Models with Extreme Learning Machines’, Neurocomputing, 102, pp. 111–124. doi: 10.1016 /j.neucom.2011.12. 055.

CAO, J. dkk. 2018 ‘Extreme Learning Machine with Affine Transformation Inputs in an Activation Function’, IEEE Transactions on Neural Networks and Learning Systems. IEEE, PP(November), pp. 1–15. doi: 10.1109/ TNNLS.2018.2877468.

CHOLISSODIN, I. dkk. (2017) ‘Optimasi Kandungan Gizi Susu Kambing Peranakan Etawa ( PE ) Menggunakan Elm-Pso Di Upt Pembibitan Ternak Dan Hijauan’, 4(1), pp. 31–36.

HASSAN, D. 2017 ‘Cost-Sensitive Access Control for Detecting Remote to Local ( R2L ) and User to Root ( U2R ) Attacks’, 43(2), pp. 124–129.

HUANG, G. BIN, ZHU, Q. Y. & SIEW, C. K. 2006 ‘Extreme Learning Machine: Theory and Applications’, Neurocomputing, 70(1–3), pp. 489–501. doi: 10.1016/j.neucom.2005. 12.126.

KUMAR, G. 2014 ‘Understanding Denial of Service (DoS) Attacks Using OSI Reference Model’, (5), pp. 10–17.

KUMAR, S. dkk. 2014 ‘A Detail Analysis on Intrusion Detection Datasets’, (May). doi: 10.1109/IAdCC .2014.6779523.

LIAO, H. J. dkk. 2013 ‘Intrusion Detection System: A Comprehensive Review’, Journal of Network and Computer Applications. Elsevier, 36(1), pp. 16–24. doi: 10.1016 /j.jnca.2012.09.004.

NSKH, P., M, N. V. & NAIK, R. R. 2016 ‘Principle Component Analysis based Intrusion Detection System Using Support Vector Machine’, 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). IEEE, pp. 1344–1350. doi: 10.1109 /RTEICT.2016.7808050.

RODRIGUEZ, D. (2019) ‘Distributed Correlation-Based Feature Selection in Spark’, pp. 1–25. Information Sciences. Elsevier https://doi.org /10.1016/j.ins. 2018.10.052

STROKA, J. & ANKLAM, E. 2002 ‘Comparison of two post-column derivatization systems, ultraviolet irradiation and electrochemical determination, for the liquid chromatographic determination of aflatoxins in food’, Journal of AOAC International, 85(2).

QU, D. dkk. 2018 ‘Journal of Network and Computer Applications A cache-aware social-based QoS routing scheme in Information Centric Networks’, Journal of Network and Computer Applications. Elsevier Ltd, 121(January), pp. 20–32. doi: 10.1016/j.jnca. 2018.07.002.

SECURITY, I. & Report, T. n.d [online] 2017 ‘Internet Security Treat Report’ Tersedia di <https://www.symantec.com/content/dam/symantec/docs/reports/istr-22-2017-en.pdf>, 22 (April).

SINGH, D. DAN SINGH, B. 2019 ‘Investigating the Impact of Data Normalization On Classification Performance’, Applied Soft Computing Journal. Elsevier B.V., p. 105524. doi: 10.1016/j.asoc. 2019.105524.

STOLFO, S. et al. 2000 ‘Cost-based modeling for fraud and intrusion detection: results from the jam project, in: DARPA Information Survivability Conference and Exposition’, DISCEX’00, Proceedings, 2(IEEE, 2000), pp. 130–144.

CHOU, Y. K. 2007 ‘Correlation-Based Feature Selection for Intrusion Detection Design Te-Shun Chou, Kang K. Yen, and Jun Luo’, pp. 1–7.

Diterbitkan

04-02-2021

Terbitan

Bagian

Ilmu Komputer

Cara Mengutip

Metode Deteksi Intrusi Menggunakan Algoritme Extreme Learning Machine dengan Correlation-based Feature Selection. (2021). Jurnal Teknologi Informasi Dan Ilmu Komputer, 8(1), 103-110. https://doi.org/10.25126/jtiik.0813358