scholarly journals Klasifikasi Anomali Intrusion Detection System (IDS) Menggunakan Algoritma Naïve Bayes Classifier dan Correlation-Based Feature Selection

2019 ◽  
Vol 2 (4) ◽  
pp. 135
Author(s):  
Saipul Anwar ◽  
Fajar Septian ◽  
Ristasari Dwi Septiana
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Naive Bayes ◽  
Detection System ◽  
Naïve Bayes ◽  
Attribute Selection ◽  
Correlation Technique ◽  
Correlation Based Feature Selection ◽  
Bayes Algorithm

Intrusion Detection System (IDS) is useful for detecting an attack or disturbance on a network or information system. Anomaly detection is a type of IDS that can detect a deviate attack on the network based on statistical probability. The increasing use of the internet also increases interference or attacks from intruders or crackers that exploit weak internet protocols and application software. When many data packets arrive, a problem arises that needs to be analyzed. The right technique to analyze the data package is data mining. This study aims to classify IDS anomalies using the Naïve Bayes classification algorithm from the results of attribute selection with correlation-based feature selection. This study uses a UNSW-NB15 intrusion detection system data collection consisting of 49 attributes and 321,283 data records. Performance measurements are based on accuracy, precision, F-Measure and ROC Area. The results of attribute selection with correlation-based feature selection leave 4 attributes. The results of the evaluation of IDS anomaly classification using the naïve Bayes algorithm without the precedence of the attributes selected by the correlation technique obtained an accuracy rate of 71.2%. While the classification results if preceded by the attributes selected by the correlation technique obtained an accuracy of 74.8%. Classification with the naïve Bayes algorithm can be improved its accuracy which is preceded by the selection of attributes with correlation techniques.

scholarly journals Intelligent Intrusion Detection System using Supervised Learning

2021 ◽  
Author(s):  
Sandipan Roy ◽  
Apurbo Mandal ◽  
Debraj Dey
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Naive Bayes ◽  
Detection System ◽  
Classification Problem ◽  
Naïve Bayes ◽  
Support Vector ◽  
Detection Systems ◽  
Malicious Activity ◽  
Bayes Algorithm

Going digital involves networking with so many connected devices, so network security becomes a critical task for everyone. But an intrusion detection system can help us to detect malicious activity in a system or network. But generally, intrusion detection systems (IDS) are not reliable and sustainable also they require more resources. In recent years so many machine learning methods are proposed to give higher accuracy with minimal false alerts. But analyzing those huge traffic data is still challenging. So, in this article, we proposed a technique using the Support Vector Machine & Naive Bayes algorithm, by using this we can solve the classification problem of the intrusion detection system. For evaluating our proposed method, we use NSL-KDD and UNSW-NB15 dataset. And after getting the result we see that the SVM works better than the Naive Bayes algorithm on that dataset.

scholarly journals Naive Bayes modification for intrusion detection system classification with zero probability

2021 ◽  
Vol 10 (5) ◽  
pp. 2751-2758
Author(s):  
Yogiek Indra Kurniawan ◽  
Fakhrur Razi ◽  
Nofiyati Nofiyati ◽  
Bangun Wijayanto ◽  
Muhammad Luthfi Hidayat
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Naive Bayes ◽  
Detection System ◽  
Naïve Bayes ◽  
Multiplication Operation ◽  
Bayes Algorithm ◽  
Research Show ◽  
Better Than

One of the methods used in detecting the intrusion detection system is by implementing Naïve Bayes algorithm. However, Naïve Bayes has a problem when one of the probabilities is 0, it will cause inaccurate prediction, or even no prediction was found. This paper proposed two modifications for Naïve Bayes algorithm. The first modification eliminated the variable that has 0 probability and the second modification changed the multiplication operations to addition operations. This modification is only applied when the Naïve Bayes algorithm does not find any prediction results caused by zero probabilities. The results of this research show that the value of precision, recall, and accuracy in the modification made tends to increase and better than the original Naïve Bayes algorithm. The highest precision, recall, and accuracy are obtained from modification by changing the multiplication operation to the addition. Increasing precision can reach 4%, increasing recall reaches 2% and increasing accuracy reaches 2%.

scholarly journals Feature Dependent Naïve Bayes For Network Intrusion Detection System

2020 ◽  
Vol 1641 ◽  
pp. 012023
Author(s):  
Panny Agustia Rahayuningsih ◽  
Reza Maulana ◽  
Windi Irmayani ◽  
Dedi Saputra ◽  
Deasy Purwaningtias
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Naive Bayes ◽  
Detection System ◽  
Naïve Bayes ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Network Intrusion Detection System

scholarly journals Optimasi Algoritma Naïve Bayes Classifier untuk Mendeteksi Anomaly dengan Univariate Fitur Selection

2020 ◽  
Vol 4 (2) ◽  
pp. 40-49
Author(s):  
Harianto Harianto ◽  
◽  
Andi Sunyoto ◽  
Sudarmawan Sudarmawan ◽  
◽  
...  
Keyword(s):  
Feature Selection ◽  
Intrusion Detection System ◽  
Naive Bayes ◽  
Detection System ◽  
Naïve Bayes ◽  
Training Data ◽  
Bayes Classifier ◽  
Data Set ◽  
System Data ◽  
And Training

System and network security from interference from parties who do not have access to the system is the most important in a system. To realize a system, data or network that is safe at unauthorized users or other interference, a system is needed to detect it. Intrusion-Detection System (IDS) is a method that can be used to detect suspicious activity in a system or network. The classification algorithm in artificial intelligence can be applied to this problem. There are many classification algorithms that can be used, one of which is Naïve Bayes. This study aims to optimize Naïve Bayes using Univariate Selection on the UNSW-NB 15 data set. The features used only take 40 features that have the best relevance. Then the data set is divided into two test data and training data, namely 10%: 90%, 20%: 70%, 30%: 70%, 40%: 60% and 50%: 50%. From the experiments carried out, it was found that feature selection had quite an effect on the accuracy value obtained. The highest accuracy value is obtained when the data set is divided into 40%: 60% for both feature selection and non-feature selection. Naïve Bayes with unselected features obtained the highest accuracy value of 91.43%, while with feature selection 91.62%, using feature selection could increase the accuracy value by 0.19%.

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