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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Pengabdian Pada Masyarakat Computer Based Information System Journal Jurnal Puan Indonesia
Andi Maslan
Universitas Putera Batam
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Other

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Journal : IAES International Journal of Artificial Intelligence (IJ-AI)

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Feature selection for DDoS detection using classification machine learning techniques Andi Maslan; Kamaruddin Malik Bin Mohamad; Feresa Binti Mohd Foozy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (581.324 KB) | DOI: 10.11591/ijai.v9.i1.pp137-145

Abstract

Computer system security is a factor that needs to be considered in the era of industrial revolution 4.0, namely by preventing various threats to the system, as well as being able to detect and repair any damage that occurs to the computer system. DDoS attacks are a threat to the company at this time because this attack is carried out by making very large requests for a site or website server so that the system becomes stuck and cannot function at all. DDoS attacks in Indonesia and developed countries always increase every year to 6% from only 3%. To minimize the attack, we conducted a study using Machine Learning techniques. The dataset is obtained from the results of DDoS attacks that have been collected by the researchers. From the datasets there is a training and testing of data using five techniques classification: Neural Network, Naïve Bayes and Random Forest, KNN, and Support Vector Machine (SVM), datasets processed have different percentages, with the aim of facilitating in classifying. From this study it can be concluded that from the five classification techniques used, the Forest random classification technique achieved the highest level of accuracy (98.70%) with a Weighted Avg 98.4%. This means that the technique can detect DDoS attacks accurately on the application that will be developed.
Co-Authors Ageng Rara Cindoswari Angel Purwanti Auric Auric Elisa, Erlin Era Azriani Feresa Binti Mohd Foozy Kamaruddin Malik Bin Mohamad Liberniat Gea Qori Framana Raymond Raymond Stevander Glenn Jeremiah Leong