DETECTION AND MITIGATION OF DISTRIBUTED DENIAL OF SERVICE ATTACKS ON NETWORK ARCHITECTURE SOFTWARE DEFINED NETWORKING USING THE NAIVE BAYES ALGORITHM

DDoS attacks are a form of attack carried out by sending packets continuously to machines and even computer networks. This attack will result in a machine or network resources that cannot be accessed or used by users. DDoS attacks usually originate from several machines operated by users or by bots, whereas Dos attacks are carried out by one person or one system. In this study, the term to be used is the term DDoS to represent a DoS or DDoS attack. In the network world, Software Defined Network (SDN) is a promising paradigm. SDN separates the control plane from forwarding plane to improve network programmability and network management. As part of the network, SDN is not spared from DDoS attacks. In this study, we use the naïve Bayes algorithm as a method to detect DDoS attacks on the Software Defined Network network architecture

Water quality prediction based on Naïve Bayes algorithm

In the fast-changing world with increased water demand, water pollution, environmental problems, and related data, information on water quality and suitability for any purpose should be prompt and reliable. Traditional approaches often fail in the attempt to predict water quality classes and new ones are needed to handle a large amount or missing data to predict water quality in real-time. One of such approaches is machine-learning (ML) based prediction. This paper presents the results of the application of the Naïve Bayes, a widely used ML method, in creating the prediction model. The proposed model is based on nine water quality parameters: temperature, pH value, electrical conductivity, oxygen saturation, biological oxygen demand, suspended solids, nitrogen oxides, orthophosphates, and ammonium. It is created in software Netica and tested and verified using the data covering the period 2013–2019 from five locations in Vojvodina Province, Serbia. Forty-eight samples are used to train the model. Once trained, the Naïve Bayes model correctly predicted the class of water sample in 64 out of 68 cases, including cases with missing data. This recommends it as a trustful tool in the transition from traditional to digital water management.

SISTEM REKOMENDASI TEMPAT WISATA MENGGUNAKAN ALGORITMA CHEAPEST INSERTION HEURISTIC DAN NAÏVE BAYES

Bali is one of the best tourism areas in Indonesia, as evidenced in 2016 Bali received a number of awards on the TripAdvisor Travelers Choice Award in global and Asian scope. However, the Corona virus outbreak from 2019, caused the tourism sector in Bali to decline, thus a solution is needed to restore the tourism sector in Bali, where one solution is to increase cultural tourism to the maximum, as the main attraction of tourist destinations in Bali. Bali. So the author proposes a tourism recommendation system, which aims to recommend tourist attractions that are suitable for tourists, which in this recommendation system is also recommended cultural tourism destinations that are directly recommended by the community, and there is also a mapping of tourist attractions as part of a tourist recommendation system, mapping of tourist attractions public and cultural attractions. In this tourism recommendation system, using the Naïve Bayes Algorithm to recommend general tourist destinations based on the personal motivation of tourists, which is based on the attributes of age, gender, natural interest, artificial interest, cultural interest of tourists, using 200 training data consisting of 14 classes of tourist attractions. . In addition, this tourist recommendation system is equipped with recommendations for routing tourist attractions using the Cheapest Insertion Heuristic Algorithm, to arrange a list of tourist attractions. Keywords: Recommendation System, Naïve Bayes Algorithm, Cheapest Insertion Heuristic Algorithm, Personal Motivation, Place Mapping.

Assessment of risk factors in medical data using improved Binary Artificial Fish Swarm Algorithm with Classification upon Evaluation from F-Test

The objective of this research work is to effectively deploy improved Binary Artificial Fish Swarm optimization Algorithm (BAFSA) with the data classification techniques. The improvement has been made with accordance to the condition of visual scope and the movement of fish to update towards the central position and chasing behavior towards best point of movement has been modified among the given population. The experimental results show that feature selection by BAFSA and classification by Decision trees and Gaussian Naïve bayes algorithm provides an improved accuracy of about 89.6% for Pima Indian diabetic dataset, 91.1% for lenses dataset and 94.4% for heart disease dataset. Statistical analysis has also been made using Fisher’s F-Test for two sample variance and the selected risk factors such as glucose, insulin level, blood pressure for diabetics datasets, spectacle prescription, tear production rate for lenses dataset and trestbps, cholesterol level, thalach, chest pain type for heart disease dataset are found to be significant with R2<0.001 respectively.

Assessment of risk factors in medical data using improved Binary Artificial Fish Swarm Algorithm with Classification upon Evaluation from F-Test

The objective of this research work is to effectively deploy improved Binary Artificial Fish Swarm optimization Algorithm (BAFSA) with the data classification techniques. The improvement has been made with accordance to the condition of visual scope and the movement of fish to update towards the central position and chasing behavior towards best point of movement has been modified among the given population. The experimental results show that feature selection by BAFSA and classification by Decision trees and Gaussian Naïve bayes algorithm provides an improved accuracy of about 89.6% for Pima Indian diabetic dataset, 91.1% for lenses dataset and 94.4% for heart disease dataset. Statistical analysis has also been made using Fisher’s F-Test for two sample variance and the selected risk factors such as glucose, insulin level, blood pressure for diabetics datasets, spectacle prescription, tear production rate for lenses dataset and trestbps, cholesterol level, thalach, chest pain type for heart disease dataset are found to be significant with R2<0.001 respectively.

Perbandingan Kinerja Algoritma Klasifikasi Naive Bayes, Support Vector Machine (SVM), dan Random Forest untuk Prediksi Ketidakhadiran di Tempat Kerja

Absence is a problem for the company. Absenteeism is defined as a task that is assigned to an individual, but the individual cannot complete the task when he is not present. Absence from work is influenced by many factors, including mismatched working hours, job demand and other factors such as serious accidents / illness, low morale, poor working conditions, boredom, lack of supervision, personal problems, insufficient nutrition, transportation problems, stress, workload, and dissatisfaction. The purpose of this study is to predict absenteeism at work based on the Absenteeism at work dataset obtained from the UCI Machine Learning repository site using the Weka 3.8 application and the Naïve Bayes algorithm, Support Vector Machine (SVM), and Random Forest. In the results of the study, the Random Forest algorithm obtained the highest accuracy, precision, and recall values compared to the Naïve Bayes and SVM algorithms, which resulted in an accuracy value of 99.38%, 99.42% precision and a recall of 99.39%.

Application Of Naive Bayes Algorithm In Classification Of Child Nutrition At The Simalungun Health Office

Toddlers are among the most vulnerable groups to nutritional problems when viewed from the point of view of health and nutrition problems, while at this time they are experiencing a cycle of relatively rapid growth and development. .7% is quite high where the number of births is relatively large. Researchers try to classify 10 toddlers using WEKA to find out whether they have nutritional disorders or are normal by using 5 attributes as system input and a class namely nutrition which divides this class into 4 namely bad, less, good and more with the amount of training data 219 data then data compared with the actual nutritional conditions and obtained an accuracy of 60% and an error of 40% with these results it can be concluded that the accuracy is not too good. Based on this, it is hoped that the results of this classification can help further research in classifying the nutrition of children under five.

Application of Nave Bayes Algorithm for SMS Spam Classification Using Orange

Mobile phones have become a necessity for everyone. SMS is a communication service that is used to send and receive short messages in the form of text on mobile phones. Among all the advantages of SMS, there is a very annoying activity called spam (unsolicited commercial advertisements). Spam is the continuous use of electronic devices to send messages. called spammers. Spam messages are sent by advertisers with the lowest operating costs. Therefore, there are a lot of spammers and the number of messages requested is huge. Therefore, many aspects are harmed and disturbed. When SMS enters the user's mobile device, this study aims to classify spam and ham SMS. SMS classification adopts naive Bayes method. By looking at the contents of the SMS, the application of the naive Bayes method in data mining can distinguish unwanted SMS from non-spam. Results The classification accuracy rate is 0.999%. Based on the research that I have done, the Naive Bayes method can classify 1000 SMS spam data contained in the SMS spam data set file correctly.