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All Journal 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%.
Co-Authors Ahmad Azhari Arif Wirawan Muhammad