scholarly journals An Indonesian Hoax News Detection System Using Reader Feedback and Naïve Bayes Algorithm

2020 ◽  
Vol 20 (1) ◽  
pp. 82-94
Author(s):  
Badrus Zaman ◽  
Army Justitia ◽  
Kretawiweka Nuraga Sani ◽  
Endah Purwanti
Keyword(s):  
Performance Evaluation ◽  
System Performance ◽  
Naive Bayes ◽  
Detection System ◽  
Naïve Bayes ◽  
Bayes Algorithm ◽  
F Measure ◽  
System Performance Evaluation

AbstractHoax news in Indonesia spread at an alarming rate. To reduce this, hoax news detection system needs to be created and put into practice. Such a system may use readers’ feedback and Naïve Bayes algorithm, which is used to verify news. Overtime, by using readers’ feedback, database corpus will continue to grow and could improve system performance. The current research aims to reach this. System performance evaluation is carried out under two conditions ‒ with and without sources (URL). The system is able to detect hoax news very well under both conditions. The highest precision, recall and f-measure values when including URL are 0.91, 1, and 0.95 respectively. Meanwhile, the highest value of precision, recall and f-measure without URL are 0.88, 1 and 0.94, respectively.

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 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 Indonesian language email spam detection using N-gram and Naïve Bayes algorithm

2020 ◽  
Vol 9 (5) ◽  
pp. 2012-2019
Author(s):  
Yustinus Vernanda ◽  
Seng Hansun ◽  
Marcel Bonar Kristanda
Keyword(s):  
Data Exchange ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayesian Filtering ◽  
Spam Filter ◽  
N Gram ◽  
Bayes Algorithm ◽  
Rest Api ◽  
Email Spam ◽  
F Measure

Indonesia is ranked the top 8th out of the total country population in the world for the global spammers. Web-based spam filter service with the REST API type can be used to detect email spam in the Indonesian language on the email server or various types of email server applications. With REST API, then there will be data exchange between the applications with JSON data type using existing HTTP commands. One type of spam filter commonly used is Bayesian Filtering, where the Naïve Bayes algorithm is used as a classification algorithm. Meanwhile, the N-gram method is used to increase the accuracy of the implementation of the Naïve Bayes algorithm in this study. N-gram and Naïve Bayes algorithms to detect spam email in the Indonesian language have successfully been implemented with accuracy around 0.615 until 0.94, precision at 0.566 until 0.924, recall at 0.96 until 1.00, and F-measure at 0.721 until 0.942. The best solution is found by using the 5-gram method with the highest score of accuracy at 0.94, precision at 0.924, recall at 0.96, and F-measure value at 0.942.

Sea Wave Detection System Using Web-Based Naive Bayes Algorithm

2021 ◽  
Author(s):  
Bimo Falaka ◽  
Randy Erfa Saputra ◽  
Casi Setianingsih ◽  
Muhammad Ary Murti
Keyword(s):  
Naive Bayes ◽  
Detection System ◽  
Naïve Bayes ◽  
Web Based ◽  
Wave Detection ◽  
Bayes Algorithm ◽  
Sea Wave

scholarly journals Cyberbullying Detection System Using Machine Learning

2021 ◽  
Vol 9 (9) ◽  
pp. 2059-2063
Author(s):  
Ms. Shama Kabeer
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Naive Bayes ◽  
Detection System ◽  
Naïve Bayes ◽  
The Internet ◽  
Bullying Behavior ◽  
Bayes Algorithm ◽  
Cyberbullying Detection ◽  
Online Form

Abstract: Cyberbullying is an online form of harassment. By posting, commenting, sending, or distributing personal, derogatory, false, or nasty stuff about others that can shame or humiliate them, this conduct is done with the goal of harming others. Once such content is published on the internet, it remains accessible indefinitely. This activity is considered unlawful, and it is more widespread among children and teenagers. Cyberbullying is an online epidemic that has the potential to result in devastating outcomes such as violence and suicide, and so must be dealt with swiftly and properly. To detect bullying behavior in textual messages, a real-time cyberbullying detection system based on machine learning—Naïve Bayes Algorithm is presented. The model was created to determine whether a tweet was bullying or non-bullying in nature. Also, to assist victims in dealing with bullying difficulties without their identities being revealed. Keywords: Machine Learning, Cyberbullying, Naïve Bayes, Cybercrimes, Cyberbullying Detection

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%.

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