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All Journal JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research
Foozy, Cik Feresa Mohd
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Computer Science & IT Electrical & Electronics Engineering

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Machine Learning-Based Distributed Denial of Service Attack Detection on Intrusion Detection System Regarding to Feature Selection Muhammad, Arif Wirawan; Foozy, Cik Feresa Mohd; Azhari, Ahmad
International Journal of Artificial Intelligence Research Vol 4, No 1 (2020): June
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (484.09 KB) | DOI: 10.29099/ijair.v4i1.156

Abstract

Distributed Service Denial (DDoS) is a type of network attack, which each year increases in volume and intensity.  DDoS attacks also form part of the major types of cyber security threats so far. Early detection plays a key role in avoiding the catastrophic effects on server infrastructure from DDoS attacks. Detection techniques in the traditional Intrusion Detection System (IDS) are far from perfect compared to a number of modern techniques and tools used by attackers, because the traditional IDS only uses signature-based detection or anomaly-based detection models and causes a lot of false positive flags, since the flow of computer network data packets has complex properties in terms of both size and source. Based on the  deficiency in the ordinary IDS, this study aims to detect DDoS attacks by using machine learning techniques to enhance IDS policy development.  According to the experiment the selection of features plays an important role in the precision of the detection results and in the performance of machine learning in classification problems. The combination of seven key selected dataset features used as an input neural network classifier in this study provides the highest accuracy value at 97.76%.
Feature Selection Approach to Detect DDoS Attack Using Machine Learning Algorithms Azmi, Muhammad Aqil Haqeemi; Foozy, Cik Feresa Mohd; Sukri, Khairul Amin Mohamad; Abdullah, Nurul Azma; Hamid, Isredza Rahmi A.; Amnur, Hidra
JOIV : International Journal on Informatics Visualization Vol 5, No 4 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.4.734

Abstract

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.
Co-Authors Abdullah, Nurul Azma Ahmad Azhari Arif Wirawan Muhammad Azmi, Muhammad Aqil Haqeemi Hamid, Isredza Rahmi A. Hidra Amnur Sukri, Khairul Amin Mohamad