DETEKSI SERANGAN PADA INTRUSION DETECTION SYSTEM ( IDS ) UNTUK KLASIFIKASI SERANGAN DENGAN ALGORITMA NAÏVE BAYES, C.45 DAN K-NN DALAM MEMINIMALISASI RESIKO TERHADAP PENGGUNA

Niko Suwaryo, Ismasari Nawangsih, Sri Rejeki

Sari


ABSTRACT

Intrusion Detection System is the ability possessed by hardware or software that serves to detect suspicious activity on the network and analyze and search in general. The purpose of this study is to classify attack detection on the Intrusion Detection System using the C.45, Naïve Bayes and K-NN algorithms to see how big the attack is. The benefits gained in this study are as a test and learning material in analyzing, classifying attacks so that they can prevent and minimize attacks to users. To overcome this problem, this study uses the C.45 algorithm, Naïve Bayes, K-NN, K-NN algorithm produces an accuracy rate of 82.58%, Recall 81.73% and Precision 84.11% while the Naïve Bayes accuracy 96.91%, Recall 97,45% and Percision 96.18% and the algorithm produces an optimal value of C.45 accuracy 97.80% Recall 98.18% and Precision  97.60%. On the attribute (attack) which has the number of classes or normal labels, dos, probes, r21. The results of the lowest K-NN algorithm are caused or normal to be considered yes(an attack) which should be No(no attack)and the C.45 algorithm attribute(attack) normal, dos, probe and r21, normal(no attack), yes(the presence of an attack) is optimal in the classification of attack detection data on Intrusion Detection System(IDS).

 

Keywords: Data Mining, C.45, Naïve Bayes and K-NN, Intrusion Detection System(IDS)


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Referensi


Dicky Nofriansyah, Gunadi Widi Nurcahyo, Penerapan Data Mining, Jogyakarta 2015

Jupriyadi, (2018). Implementasi Seleksi Fitur Menggunakan Algoritma FVBRM Untuk Klasifikasi Serangan Pada Intrusion Detection System (IDS)

Khaerani & Handoko, (2015 ). Klasifikasi Serangan pada Intrusion Detection System (IDS) Dengan Algoritma C.45.

Kharisma Muchammad (2016). Deteksi Intrusi dengan Jumlah Jarak dari Centroid dan Sub-centroid.

Kusrini, Emah Taufiq Luthfi ( 2009 ). Algortima Data Mining. Andi Yogyakarta

Muhammad Satria Nugraha, (2010). Implementasi Intrusion Detection System (IDS) Untuk filtering Paket Data. Implementasi dan Analisa Hasil Data Mining

Oktavia Ari Marlita, Adiwijaya, Angelina Prima Kurniati, (2015). Anomaly Detection pada Intrusion Detection System (IDS) Menggunakan Metode Bayesian Network.

Osiris Villacampa, (2015).Feature Selection and Classification Methods for Decision Making: A Comparative Analysis.

Retno Tri Vulandari, 2017 . Data Mining. Gava Media.

Silalahi, Kristiani Desri., Murfi, Hendri., Satria, Yudi. (2017). Studi Data Mining. Informatika

........., Perbandingan Pemilihan Fitur untuk Support Vector Machine pada Klasifikasi

Penilaian Risiko Kredit, 1(2), 119–136.




DOI: https://doi.org/10.35968/jsi.v8i2.732

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