Perbandingan Algoritma K-Nearest Neighbour (KNN) dan Naive Bayes pada Intrusion Detection System (IDS)

Aditya Dwi Afifaturahman, Firmansyah MSN

Abstract

Machine learning techniques are widely used to develop Intrusion Detection Systems (IDS) to detect and classify cyber attacks at the network level and the host level in a timely and automated manner. However, many challenges arise as malicious attacks are constantly changing and occurring in very large volumes requiring a scalable solution. Therefore, this study conducted a comparison of the K-Nearest Neighbor (KNN) and Naive Bayes algorithms. The dataset used in this study is the Ddos features-IDS 2017 dataset published in 2019. This research analyzes the comparison of methods generated from the classification process based on metric accuracy, specificity and sensitivity parameters. The classification process using the K-Nearest Neighbor (KNN) and Naive Bayes algorithms, it can be concluded that the results of the three tests with a percentage split of 60%, 70% and 80% show that the K-Nearest Neighbor (KNN) algorithm gets a higher value than Naive Bayes except the error rate because the error rate indicates that the data failed to be classified properly. Testing on a percentage split of 60% KNN parameter accuracy gets a value of 99.53%, specificity 94.05%, sensitivity 75.20%, testing on a percentage split 70% KNN parameter accuracy gets a value of 99.69%, specificity 94.59%, sensitivity 78.40% and testing on percetage split 80%, KNN parameter accuracy parameter got a value of 99.70%, specificity 94.44%, sensitivity 75.85%.

Full Text:

PDF 17-25

References

M. F. Fibrianda and A. Bhawiyuga, “Analisis Perbandingan Akurasi Deteksi Serangan Pada Jaringan Komputer Dengan Metode Naïve Bayes Dan Support Vector Machine (SVM),” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 9, pp. 3112–3123, 2018.

R. Vinayakumar, M. Alazab, K. P. Soman, P. Poornachandran, A. Al-Nemrat, and S. Venkatraman, “Deep Learning Approach for Intelligent Intrusion Detection System,” IEEE Access, vol. 7, pp. 41525–41550, 2019.

Candra Adi Winanto, “Deteksi Serangan Denial of Service Menggunakan Artificial Immune System,” vol. 2, no. 1, pp. 456–459, 2016.

B. Agarwal and N. Mittal, “Hybrid Approach for Detection of Anomaly Network Traffic using Data Mining Techniques,” Procedia Technology, vol. 6, pp. 996–1003, 2012.

P. García-Teodoro, J. Díaz-Verdejo, G. Maciá-Fernández, and E. Vázquez, “Anomaly-based network intrusion detection: Techniques, systems and challenges,” Computers and Security, vol. 28, no. 1–2, pp. 18–28, 2009.

R. N. Devita, H. W. Herwanto, and A. P. Wibawa, “Perbandingan Kinerja Metode Naive Bayes dan K-Nearest Neighbor untuk Klasifikasi Artikel Berbahasa indonesia,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 5, no. 4, p. 427, 2018.

R. Doshi, N. Apthorpe, and N. Feamster, “Machine learning DDoS detection for consumer internet of things devices,” Proceedings - 2018 IEEE Symposium on Security and Privacy Workshops, SPW 2018, no. Ml, pp. 29–35, 2018.

I. N. Rizkiana, A. Rahmatulloh, and R. Gunawan, “Penerapan Metode Clustering K-Means Untuk Menentukan Nilai Burst Header Packet Flooding Attack Pada Optical Burst Switching,” Indonesian Journal of Applied Informatics, vol. 4, no. 2, p. 107, Aug. 2020.

Refbacks

  • There are currently no refbacks.