Studi Perbandingan Deteksi Intrusi Jaringan Menggunakan Machine Learning: (Metode SVM dan ANN)

Tony Tan; Hendi Sama; Gautama Wijaya; Osei Enoch Aboagye

doi:10.34010/jati.v13i2.10484

Tony Tan
Hendi Sama Universitas Internasional Batam
Gautama Wijaya Universitas Internasional Batam
Osei Enoch Aboagye Universitas Internasional Batam

DOI: https://doi.org/10.34010/jati.v13i2.10484

Keywords: Machine Learning, Jaringan, Deteksi Intrusi, SVM, ANN

Abstract

Machine Learning berkaitan dengan penggunaan algoritma untuk membuat mesin berfungsi. Algoritma supervised machine learning belajar pada dataset untuk membuat prediksi berdasarkan pengetahuan yang mereka peroleh saat belajar. Machine Learning memiliki dampak signifikan dalam keamanan siber. Sistem deteksi intrusi (IDS), sistem pencegahan intrusi (IPS) dan firewall tradisional membantu mendeteksi intrusi tetapi sayangnya, kebanyakan dari mereka memberi false alarm, dapat memiliki kerentanan dan dapat salah konfigurasi. Penggunaan algoritma machine learning telah terbukti lebih efektif dalam deteksi intrusi. Penelitian ini bertujuan untuk membandingkan efektivitas Algoritma Support Vector Machine (SVM) dan Artificial Neural Network (ANN) untuk intrusi deteksi. Penelitian ini menggunakan metode eksperimen dengan melatih dan menguji SVM dan ANN pada Dataset KDD Cup 99 di Google Colaboratory. Skor akurasi pelatihan dan pengujian, waktu pelatihan dan pengujian, Receiver Operating Characteristic Curve (Kurva ROC) dan kecepatan jaringan adalah parameter untuk perbandingan. Hasil dari eksperimen menunjukkan bahwa; Kedua model bagus untuk mendeteksi intrusi karena SVM dan ANN memiliki skor di atas 90%. SVM lebih efektif daripada ANN dalam deteksi intrusi dengan akurasi pelatihan dan pengujian 99,87% dan 99,81%. Juga AUC untuk SVM adalah 1 untuk semua lima kelas dan mengambil lebih sedikit waktu dalam melatih dataset.

References

C. Annamalai, “Combinatorial and Multinomial Coefficients and its Computing Techniques for Machine Learning and Cybersecurity,” The Journal of Engineering and Exact Sciences, vol. 8, no. 8, pp. 14713–01i, 2022, doi: 10.18540/jcecvl8iss8pp14713-01i.

M. Ahsan, R. Gomes, M. M. Chowdhury, and K. E. Nygard, “Enhancing Machine Learning Prediction in Cybersecurity Using Dynamic Feature Selector,” Journal of Cybersecurity and Privacy, vol. 1, no. 1, pp. 199–218, 2021, doi: 10.3390/jcp1010011.

A. Halbouni, T. S. Gunawan, M. H. Habaebi, M. Halbouni, M. Kartiwi, and R. Ahmad, Machine Learning and Deep Learning Approaches for CyberSecurity: A Review, vol. 10, no. March 2021. Springer International Publishing, 2022. doi: 10.1109/ACCESS.2022.3151248.

I. D. Aiyanyo, H. Samuel, and H. Lim, “A systematic Review of Defensive and Offensive Cybersecurity With Machine Learning,” Applied Sciences (Switzerland), vol. 10, no. 17, pp. 1–26, 2020, doi: 10.3390/app10175811.

L. Yang, J. Li, L. Yin, Z. Sun, Y. Zhao, and Z. Li, “Real-time Intrusion Detection in Wireless Network: A Deep Learning-based Intelligent Mechanism.,” IEEE Access, vol. 8, no. August 2020, pp. 170128–170139, 2020, doi: 10.1109/ACCESS.2020.3019973.

D. Upadhyay, J. Manero, M. Zaman, and S. Sampalli, “Learning Classifiers for Intrusion Detection on Power Grids,” Ieee Transactions on Network and Service Management, vol. 18, no. 1, pp. 1104–1116, 2021.

L. Le Jeune, T. Goedeme, and N. Mentens, “Machine Learning for Misuse-Based Network Intrusion Detection: Overview, Unified Evaluation and Feature Choice Comparison Framework,” IEEE Access, vol. 9, no. April 2021, pp. 63995–64015, 2021, doi: 10.1109/ACCESS.2021.3075066.

A. Kim, M. Park, and D. H. Lee, “AI-IDS: Application of Deep Learning to Real-Time Web Intrusion Detection,” IEEE Access, vol. 8, pp. 70245–70261, 2020, doi: 10.1109/ACCESS.2020.2986882.

C. Liu, Z. Gu, and J. Wang, “A Hybrid Intrusion Detection System Based on Scalable K-Means+ Random Forest and Deep Learning,” IEEE Access, vol. 9, no. May 2021, pp. 75729–75740, 2021, doi: 10.1109/ACCESS.2021.3082147.

T. Moulahi, S. Zidi, A. Alabdulatif, and M. Atiquzzaman, “Comparative Performance Evaluation of Intrusion Detection Based on Machine Learning in In-Vehicle Controller Area Network Bus,” IEEE Access, vol. 9, no. 4, pp. 99595–99605, 2021, doi: 10.1109/ACCESS.2021.3095962.

L. Nie et al., “Intrusion Detection for Secure Social Internet of Things Based on Collaborative Edge Computing: A Generative Adversarial Network-Based Approach,” IEEE Transactions on Computational Social Systems, vol. 9, no. 1, pp. 134–145, 2022, doi: 10.1109/TCSS.2021.3063538.

R. Vijayanand and D. Devaraj, “A Novel Feature Selection Method Using Whale Optimization Algorithm and Genetic Operators for Intrusion Detection System in Wireless Mesh Network,” IEEE Access, vol. 8, no. March 2020, pp. 56847–56854, 2020, doi: 10.1109/ACCESS.2020.2978035.

M. Wang, K. Zheng, Y. Yang, and X. Wang, “An Explainable Machine Learning Framework for Intrusion Detection Systems,” IEEE Access, vol. 8, no. April 2020, pp. 73127–73141, 2020, doi: 10.1109/ACCESS.2020.2988359.

M. Haggag, M. M. Tantawy, and M. M. S. El-Soudani, “Implementing a Deep Learning Model for Intrusion Detection on Apache Spark Platform.,” IEEE Access, vol. 8, no. August 2020, pp. 163660–163672, 2020, doi: 10.1109/ACCESS.2020.3019931.

A. Fatani, M. A. Elaziz, A. Dahou, M. A. A. Al-Qaness, and S. Lu, “IoT Intrusion Detection System Using Deep Learning and Enhanced Transient Search Optimization,” IEEE Access, vol. 9, no. August 2021, pp. 123448–123464, 2021, doi: 10.1109/ACCESS.2021.3109081.

S. Zhang, X. Xie, and Y. Xu, “A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity,” IEEE Access, vol. 8, no. July 2021, pp. 128250–128263, 2020, doi: 10.1109/ACCESS.2020.3008433.

G. Apruzzese et al., “The Role of Machine Learning in Cybersecurity,” Digital Threats: Research and Practice, vol. 4, no. 1, 2023, doi: 10.1145/3545574.

I. Chua, T.H., Salam, “Evaluation of Machine Learning Algorithms in Network-Based Intrusion Detection System.,” Lecture Notes in Electrical Engineering, vol. 740, no. 2021, pp. 229–236, 2021, doi: 10.1007/978-981-33-6393-9_24.

S. Sajja, G., Mustafa, M., Ponnusamy, R., Abdufattokhov, “Machine Learning Algorithms in Intrusion Detection and Classification,” Annals of the Romanian Society for Cell Biology, vol. 25, no. 6, pp. 12211–12219, 2021, [Online]. Available: http://annalsofrscb.ro/index.php/journal/article/view/7837

K. S. Kajal, A., Nandal, “A Hybrid Approach for Cyber Security: Improved Intrusion Detection System Using ANN-SVM,” Indian Journal of Computer Science and Engineering, vol. 11, no. 4, pp. 412–425, 2020, doi: 10.21817/indjcse/2020/v11i4/201104300.

V. Sstla, V. K. K. Kolli, L. K. Voggu, R. Bhavanam, and S. Vallabhasoyula, “Predictive Model for Network Intrusion Detection System Using Deep Learning,” Revue d’Intelligence Artificielle, vol. 34, no. 3, pp. 323–330, 2020, doi: 10.18280/ria.340310.

A. M. Salih, A.A., Abdulazeez, “Evaluation of Classification Algorithms for Intrusion Detection System: A Review,” Journal of Soft Computing and Data Mining, vol. 2, no. 1, pp. 31–40, 2021, doi: 10.30880/jscdm.2021.02.01.004.

C. C. O’Mahony, D.G., Curran, T.J., Harris, J.P., Murphy, “Interference and Intrusion in Wireless Sensor Networks,” IEEE Aerospace and Electronic Systems Magazine, vol. 35, no. 2, pp. 1–14, 2020, doi: 10.1109/MAES.2020.2970262.

G. C. Padmaja, B., Sravan, K.S., Patro, E.K.R., Sekhar, “A System to Automate the Development of Anomaly-Based Network Intrusion Detection Model,” Journal of Physics: Conference Series, vol. 2089, no. 1, pp. 1–15, 2021, doi: 10.1088/1742-6596/2089/1/012006.

B. Venkataramana, K. C. Mouli, and A. Eenaja, “Network Intrusion Detection By SVM & ANN With Feature Selection,” International Journal of Creative Research Thoughts (IJCRT), vol. 8, no. 6, pp. 3704–3708, 2020, [Online]. Available: http://ijcrt.org/viewfull.php?&p_id=IJCRT2006506

N. Alagrash, Y., Drebee, A., Zirjawi, “Comparing the Area of Data Mining Algorithms in Network Intrusion Detection,” Journal of Information Security, vol. 11, no. 1, pp. 1–18, 2020, doi: 10.4236/jis.2020.111001.

T. S. Ustun, S. M. Suhail Hussain, A. Ulutas, A. Onen, M. M. Roomi, and D. Mashima, “Machine Learning-based Intrusion Detection for Achieving Cybersecurity in Smart Grids Using IEC 61850 GOOSE Messages,” Symmetry, vol. 13, no. 5, pp. 1–15, 2021, doi: 10.3390/sym13050826.

A. Imran, H., Zulfiqar, M.A., Arshad, “Machine Learning Based Intrusion Detection System,” Journal of Computational and Cognitive Engineering, vol. 2, no. 2, pp. 140–149, 2021, doi: 10.4018/978-1-7998-3327-7.ch011.

M. M. Mijwil, I. E. Salem, and M. M. Ismaeel, “The Significance of Machine Learning and Deep Learning Techniques in Cybersecurity: A Comprehensive Review,” Iraqi Journal for Computer Science and Mathematics, vol. 4, no. 1, pp. 87–101, 2023, doi: 10.52866/ijcsm.2023.01.01.008.

C. F. M. Maseer, K.Z., Yusof, R., Bahaman, N., Mostafa, S.A., Foozy, “Benchmarking of Machine Learning for Anomaly Based Intrusion Detection Systems in the CICIDS2017 Dataset,” IEEE Access, vol. 9, no. 22 January, pp. 22351–22370, 2021, doi: 10.1109/ACCESS.2021.3056614.

M. Elsisi, M. Q. Tran, K. Mahmoud, Di. E. A. Mansour, M. Lehtonen, and M. M. F. Darwish, “Towards Secured Online Monitoring for Digitalized GIS against Cyber-Attacks Based on IoT and Machine Learning,” IEEE Access, vol. 9, no. May 2021, pp. 78415–78427, 2021, doi: 10.1109/ACCESS.2021.3083499.

Y. Kasongo, M.S., Sun, “Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset,” Journal of Big Data, vol. 7, no. 1, pp. 1–20, 2020, doi: 10.1186/s40537-020-00379-6.

S. B. Dilip, R., Samanvita, N., Pramodhini, R., Vidhya, G.S., Telkar, “Performance Analysis of Machine Learning Algorithms in Intrusion Detection and Classification,” Communications in Computer and Information Science, vol. 1591 CCIS, no. May, pp. 283–289, 2022, doi: 10.1007/978-3-031-07012-9_25.

J. Lansky et al., “Deep Learning-Based Intrusion Detection Systems: A Systematic Review,” IEEE Access, vol. 9, no. July 2021, pp. 101574–101599, 2021, doi: 10.1109/ACCESS.2021.3097247.

K. Shin, Y., Kim, “Comparison of Anomaly Detection Accuracy of Host-Based Intrusion Detection Systems Based on Different Machine Learning Algorithms,” International Journal of Advanced Computer Science and Applications, vol. 11, no. 2, pp. 252–259, 2020, doi: 10.14569/ijacsa.2020.0110233.

Y. K. Rokade, M.D., Sharma, “MLIDS: A Machine Learning Approach for Intrusion Detection for Real Time Network Dataset,” 2021 International Conference on Emerging Smart Computing and Informatics, ESCI 2021, vol. 15, no. March, pp. 533–536, 2021, doi: 10.1109/ESCI50559.2021.9396829.

S. Dini, P., Saponara, “Analysis, Design, and Comparison of Machine-Learning Techniques for Networking Intrusion Detection,” Designs, vol. 5, no. 1, pp. 1–22, 2021, doi: 10.3390/designs5010009.

K. Shaukat, S. Luo, V. Varadharajan, I. A. Hameed, and M. Xu, “A Survey on Machine Learning Techniques for Cyber Security in the Last Decade,” IEEE Access, vol. 8, no. 2020, pp. 222310–222354, 2020, doi: 10.1109/ACCESS.2020.3041951.

A. Sharma, P.K., Gosain, D., Sagar, H., Kumar, C., Dogra, “SiegeBreaker: An SDN Based Practical Decoy Routing System,” Proceedings on Privacy Enhancing Technologies, vol. 2020, no. 3, pp. 243–263, 2020, doi: 10.2478/popets-2020-0051.

Y. Butt, M., Ajmal, Z., Khan, Z.I., Idrees, M., Javed, “An In-Depth Survey of Bypassing Buffer Overflow Mitigation Techniques,” Applied Sciences (Switzerland), vol. 12, no. 13, pp. 1–31, 2022, doi: 10.3390/app12136702.

P. Tanakas, A. Ilias, and N. Polemi, “A Novel System for Detecting and Preventing SQL Injection and Cross-Site-Script,” International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021, vol. 1, no. September, pp. 1–7, 2021, doi: 10.1109/ICECET52533.2021.9698688.

N. F. Syed, Z. Baig, A. Ibrahim, and C. Valli, “Denial of Service Attack Detection Through Machine Learning for the IoT,” Journal of Information and Telecommunication, vol. 4, no. 4, pp. 482–503, 2020, doi: 10.1080/24751839.2020.1767484.