Analisis Data Log IDS Snort dengan Algoritma Clustering Fuzzy C-Means
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https://doi.org/10.24843/MITE.2020.v19i01.P14
Abstrak
Snort is one of open source IDS to detect intrusion or potentially malicious activity on network traffic. Snort will give alert for every detected intrusion and write the alerts in log. Log data in IDS Snort will help network administrator to analyze the vulnerability of network security system. Clustering algorithm such as FCM can be used to analyze the log data of IDS Snort. Implementation of the algorithm is based on Python 3 and aims to cluster alerts in log data into 4 risk categories, such as low, medium, high, and critical. The outcome of this analysis is to show cluster results of FCM and to visualize the types of attacks that IDS Snort has successfully detected. Evaluation process is done by using Modified Partition Coefficient (MPC) to determine the validity of FCM.
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Referensi
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[2] A. Y. Ananta, “Seleksi Notifikasi Serangan Berbasis Ids Snort Menggunakan Metode K-Means,” SMARTICS J., vol. 3, no. 2, pp. 31–37, 2017, doi: 10.21067/smartics.v3i2.1954.
[3] K. Nalavade and B. B. Meshram, “Evaluation of K-Means Clustering for Effective Intrusion Detection and Prevention in Massive Network Traffic Data,” Int. J. Comput. Appl., vol. 96, no. 7, pp. 9–14, 2014, doi: 10.5120/16804-6526.
[4] D. Mongkareng, N. A. Setiawan, and A. E. Permanasari, “Implementasi Data Mining dengan Seleksi Fitur untuk Klasifikasi Serangan pada Intrusion Detection System (IDS),” Citee, no. gambar 2, pp. 314–321, 2017.
[5] R. Hadi, I. K. G. D. Putra, and I. N. S. Kumara, “Penentuan Kompetensi Mahasiswa dengan Algoritma Genetik dan Metode Fuzzy C-Means,” Maj. Ilm. Teknol. Elektro, vol. 15, no. 2, pp. 101–106, 2016, doi: 10.24843/mite.1502.15.
[6] N. Nidyashofa and Deden Istiawan, “Penerapan Algoritma Fuzzy C-Means untuk Pengelompokan Kabupaten / Kota di Jawa Tengah Berdasarkan Status Kesejahteraan Tahun 2015,” 6th Univ. Res. Colloq., no. November, pp. 23–30, 2017.
[7] U. M. Pak and B. Darmajaya, “Penentuan Penerima Beasiswa Dengan Algoritma Fuzzy C-Means Di Universitas Megow Pak Tulang Bawang,” J. Teknol. Inf. Magister Darmajaya, vol. 1, no. 02, pp. 158–174, 2015.
[8] M. T. A. C. Widiyanto, “Perbandingan Validitas Fuzzy Clustering pada Fuzzy C-Means Dan Particle Swarms Optimazation ( PSO ) pada Pengelompokan Kelas,” JISKa, vol. 4, no. 1, pp. 22–37, 2019.
[9] M. M. A. Amirah, A. W. Widodo, and C. Dewi, “Pengelompokan Lagu Berdasarkan Emosi Menggunakan Algoritma Fuzzy C-Means,” J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 1, no. 12, pp. 1526–1534, 2017.
[10] C. El Mostapha, M. Moughit, and Y. I. Khamlichi, “Building an efficient alert management model for intrusion detection systems,” Adv. Sci. Technol. Eng. Syst., vol. 3, no. 1, pp. 18–24, 2018, doi: 10.25046/aj030103.
[11] W. Goralski, “User Datagram Protocol,” Illus. Netw., pp. 289–306, 2017, doi: 10.1016/b978-0-12-811027-0.00011-4.
[12] K. H. Mchatta, “Ethical Hacking for Beginners ( Tools , Enumeration and Exploitation ),” no. July, 2019.
[13] S. G. Kassa, “IT Asset Valuation, Risk Assessment and Control Implementation Model,” ISACA, vol. 3, pp. 1–9, 2017.
[14] NIST, “NIST Special Publication 800-30 Revision 1 - Guide for Conducting Risk Assessments,” NIST Spec. Publ., no. September, p. 95, 2012, doi: 10.6028/NIST.SP.800-30r1.
[15] E. M. Chakir, M. Moughit, and Y. I. Khamlichi, “An Effecient Method for Evaluating Alerts of Intrusion Detection Systems National School of Applied Sciences USMBA,” 2017 Int. Conf. Wirel. Technol. Embed. Intell. Syst., pp. 1–6, 2017, doi: 10.1109/WITS.2017.7934678.
[16] M. Roesch, “SNORT Users Manual 2.9.13 The Snort Project,” p. 269, 2019.
[17] M. L. D. Dias, “fuzzy-c-means: An implementation of Fuzzy C-means clustering algorithm.” 2019.
[2] A. Y. Ananta, “Seleksi Notifikasi Serangan Berbasis Ids Snort Menggunakan Metode K-Means,” SMARTICS J., vol. 3, no. 2, pp. 31–37, 2017, doi: 10.21067/smartics.v3i2.1954.
[3] K. Nalavade and B. B. Meshram, “Evaluation of K-Means Clustering for Effective Intrusion Detection and Prevention in Massive Network Traffic Data,” Int. J. Comput. Appl., vol. 96, no. 7, pp. 9–14, 2014, doi: 10.5120/16804-6526.
[4] D. Mongkareng, N. A. Setiawan, and A. E. Permanasari, “Implementasi Data Mining dengan Seleksi Fitur untuk Klasifikasi Serangan pada Intrusion Detection System (IDS),” Citee, no. gambar 2, pp. 314–321, 2017.
[5] R. Hadi, I. K. G. D. Putra, and I. N. S. Kumara, “Penentuan Kompetensi Mahasiswa dengan Algoritma Genetik dan Metode Fuzzy C-Means,” Maj. Ilm. Teknol. Elektro, vol. 15, no. 2, pp. 101–106, 2016, doi: 10.24843/mite.1502.15.
[6] N. Nidyashofa and Deden Istiawan, “Penerapan Algoritma Fuzzy C-Means untuk Pengelompokan Kabupaten / Kota di Jawa Tengah Berdasarkan Status Kesejahteraan Tahun 2015,” 6th Univ. Res. Colloq., no. November, pp. 23–30, 2017.
[7] U. M. Pak and B. Darmajaya, “Penentuan Penerima Beasiswa Dengan Algoritma Fuzzy C-Means Di Universitas Megow Pak Tulang Bawang,” J. Teknol. Inf. Magister Darmajaya, vol. 1, no. 02, pp. 158–174, 2015.
[8] M. T. A. C. Widiyanto, “Perbandingan Validitas Fuzzy Clustering pada Fuzzy C-Means Dan Particle Swarms Optimazation ( PSO ) pada Pengelompokan Kelas,” JISKa, vol. 4, no. 1, pp. 22–37, 2019.
[9] M. M. A. Amirah, A. W. Widodo, and C. Dewi, “Pengelompokan Lagu Berdasarkan Emosi Menggunakan Algoritma Fuzzy C-Means,” J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 1, no. 12, pp. 1526–1534, 2017.
[10] C. El Mostapha, M. Moughit, and Y. I. Khamlichi, “Building an efficient alert management model for intrusion detection systems,” Adv. Sci. Technol. Eng. Syst., vol. 3, no. 1, pp. 18–24, 2018, doi: 10.25046/aj030103.
[11] W. Goralski, “User Datagram Protocol,” Illus. Netw., pp. 289–306, 2017, doi: 10.1016/b978-0-12-811027-0.00011-4.
[12] K. H. Mchatta, “Ethical Hacking for Beginners ( Tools , Enumeration and Exploitation ),” no. July, 2019.
[13] S. G. Kassa, “IT Asset Valuation, Risk Assessment and Control Implementation Model,” ISACA, vol. 3, pp. 1–9, 2017.
[14] NIST, “NIST Special Publication 800-30 Revision 1 - Guide for Conducting Risk Assessments,” NIST Spec. Publ., no. September, p. 95, 2012, doi: 10.6028/NIST.SP.800-30r1.
[15] E. M. Chakir, M. Moughit, and Y. I. Khamlichi, “An Effecient Method for Evaluating Alerts of Intrusion Detection Systems National School of Applied Sciences USMBA,” 2017 Int. Conf. Wirel. Technol. Embed. Intell. Syst., pp. 1–6, 2017, doi: 10.1109/WITS.2017.7934678.
[16] M. Roesch, “SNORT Users Manual 2.9.13 The Snort Project,” p. 269, 2019.
[17] M. L. D. Dias, “fuzzy-c-means: An implementation of Fuzzy C-means clustering algorithm.” 2019.
Diterbitkan
2020-10-15
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DEWI PARAMITHA, Ida Ayu Shinta; SASMITA, Gusti Made Arya; RAHARJA, I Made Sunia.
Analisis Data Log IDS Snort dengan Algoritma Clustering Fuzzy C-Means.
Jurnal Teknologi Elektro, [S.l.], v. 19, n. 1, p. 95-100, oct. 2020.
ISSN 2503-2372.
Tersedia pada: <https://ojs.unud.ac.id/index.php/mite/article/view/58376>. Tanggal Akses: 15 dec. 2025
doi: https://doi.org/10.24843/MITE.2020.v19i01.P14.
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