Open AccessFeature Selection Approach to Detect DDoS Attack Using Machine Learning Algorithms
Author(s) -
Muhammad Aqil Haqeemi Azmi,
Cik Feresa Mohd Foozy,
Khairul Amin Mohamad Sukri,
Nurul Azma Abdullah,
Isredza Rahmi A. Hamid,
Hidra Amnur
Publication year - 2021
Publication title -
joiv : international journal on informatics visualization
Language(s) - English
Resource type - Journals
eISSN - 2549-9904
pISSN - 2549-9610
DOI - 10.30630/joiv.5.4.734
Subject(s) - denial of service attack , computer science , naive bayes classifier , feature selection , machine learning , artificial intelligence , application layer ddos attack , feature (linguistics) , data mining , the internet , network security , algorithm , computer security , support vector machine , linguistics , philosophy , world wide web
Distributed Denial of Service (DDoS) attacks are dangerous attacks that can cause disruption to server, system or application layer. It will flood the target server with the amount of Internet traffic that the server could not afford at one time. Therefore, it is possible that the server will not work if it is affected by this DDoS attack. Due to this attack, the network security environment becomes insecure with the possibility of this attack. In recent years, the cases related to DDoS attacks have increased. Although previously there has been a lot of research on DDoS attacks, cases of DDoS attacks still exist. Therefore, the research on feature selection approach has been done in effort to detect the DDoS attacks by using machine learning techniques. In this paper, to detect DDoS attacks, features have been selected from the UNSW-NB 15 dataset by using Information Gain and Data Reduction method. To classify the selected features, ANN, Naïve Bayes, and Decision Table algorithms were used to test the dataset. To evaluate the result of the experiment, the parameters of Accuracy, Precision, True Positive and False Positive evaluated the results and classed the data into attacks and normal class. Hence, the good features have been obtained based on the experiments. To ensure the selected features are good or not, the results of classification have been compared with the past research that used the same UNSW-NB 15 dataset. To conclude, the accuracy of ANN, Naïve Bayes and Decision Table classifiers has been increased by using this feature selection approach compared to the past research.