scholarly journals K-Means Clustering and Naive Bayes Classification for Intrusion Detection

2016 ◽  
Vol 4 (1) ◽  
pp. 13-25 ◽  
Author(s):  
Z. Muda ◽  
W. Yassin ◽  
M.N. Sulaiman ◽  
N.I. Udzir
Keyword(s):  
Intrusion Detection ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Intrusion Detection Systems ◽  
False Alarms ◽  
Bayes Classifier ◽  
Detection Systems ◽  
Naive Bayes Classification ◽  
Naïve Bayes Classification ◽  
Accuracy Rates

Intrusion detection systems (IDS) effectively complement other security mechanisms by detecting malicious activities on a computer or network, and their development is evolving at an extraordinary rate. The anomaly-based IDS, which uses learning algorithms, allows detection of unknown attacks. Unfortunately, the major challenge of this approach is to minimize false alarms while maximizing detection and accuracy rates. To overcome this problem, we propose a hybrid learning approach through the combination of K-Means clustering and Naïve Bayes classification. K-Means clustering is used to cluster all data into the corresponding group based on data behavior, i.e. malicious and non-malicious, while the Naïve Bayes classifier is used to classify clustered data into correct categories, i.e. R2L, U2R, Probe, DoS and Normal. Experiments have been carried out to evaluate the performance of the proposed approach using KDD Cup ’99 dataset. The results showed that our proposed approach significantly improves the accuracy, detection rate up to 99.6% and 99.8%, respectively, while decreasing false alarms to 0.5%.

scholarly journals Klasifikasi Tingkat Rumah Tangga Miskin Saat Pandemi Dengan Naïve Bayes Classifier

2021 ◽  
Vol 7 (2) ◽  
pp. 165-173
Author(s):  
Harliana Harliana ◽  
Fatra Nonggala Putra
Keyword(s):  
Cross Validation ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Naive Bayes Classification ◽  
Naïve Bayes Classification ◽  
Fold Cross Validation

Secara definisi kemiskinan merupakan suatu kondisi individu ditingkat rumah tangga yang dinilai berdasarkan karaktersitik kemiskinan. Sebagai dampak dari pandemi covid-19 prosentase rumah tangga miskin di Indonesia meningkat sekitar 9,78%. Berdasarkan hal tersebut, maka penelitian ini akan melakukan klasifikasi dengan algoritma Naïve Bayes Classification untuk menentukan rumah tangga miskin melalui parameter survey ekonomi Nasional Tahun 2020 Modul Ketahanan Sosial yang berfokus pada pengeluaran dan konsumsi perkapita responden selama pandemic. Sedangkan tujuan dari penelitian ini adalah mendapatkan akurasi tertinggi yang dihasilkan oleh Naïve Bayes Classification dalam penentuan rumah tangga miskin. Menurut hasil pengujian dengan confusion matrix dan 10-fold cross validation didapatkan bahwa rata-rata akurasi tertinggi terjadi pada fold ke-10 dengan nilai accuracy 93,21%; precision 86,3%; dan recall 80,11%. Hal ini berarti bahwa akurasi yang dihasilkan oleh naïve bayes classifier dalam melakukan clasifikasi rumah tangga miskin cukup tinggi

scholarly journals The Comparison Combination of Naïve Bayes Classification Algorithm with Fuzzy C-Means and K-Means for Determining Beef Cattle Quality in Semarang Regency

2018 ◽  
Vol 5 (2) ◽  
pp. 194-204
Author(s):  
Feroza Rosalina Devi ◽  
Endang Sugiharti ◽  
Riza Arifudin
Keyword(s):  
Beef Cattle ◽  
Clustering Algorithm ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Fuzzy C Means ◽  
Naive Bayes Classification ◽  
Naïve Bayes Classification ◽  
Fuzzy C Means Algorithm

The beef cattle quality certainly affects the quality of meat to be consumed. This researchperforms data processing to do the classification of beef cattle quality. The data used are196 data record taken from data in 2016 and 2017. The data have 3 variables fordetermining the quality of beef cattle in Semarang regency namely age (month), Weight(Kg), and Body Condition Score (BCS) . In this research, used the combination of NaïveBayes Classification and Fuzzy C-Means algorithm also Naïve Bayes Classification andK-Means. After doing the combinations, then conducted analysis of the results of whichtype of combination that has a high accuracy. The results of this research indicate that theaccuracy of combination Naïve Bayes Classification and K-Means has a higher accuracythan the combination of Naïve Bayes Classification and Fuzzy C-Means. This can be seenfrom the combination accuracy of Fuzzy C-Means algorithm and Naïve Bayes Classifierof 96,67 while combination of K Means Clustering and Naïve Bayes Classifier algorithmis 98,33%, so it can be concluded that combination of K Means Clustering algorithm andNaïve Bayes Classifier is more recommended for determining the quality of beef cattle inSemarang regency.

Novel naïve Bayes classification models for predicting the carcinogenicity of chemicals

2016 ◽  
Vol 97 ◽  
pp. 141-149 ◽  
Author(s):  
Hui Zhang ◽  
Zhi-Xing Cao ◽  
Meng Li ◽  
Yu-Zhi Li ◽  
Cheng Peng
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Models ◽  
Naive Bayes Classification ◽  
Naïve Bayes Classification
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