Feature selection for DDoS detection using classification machine learning techniques
| Autor: | Andi Maslan, Feresa Binti Mohd Foozy, Kamaruddin Malik Mohamad |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Information Systems and Management
Artificial neural network Network security business.industry Computer science Feature selection Denial-of-service attack Classification Machine learning computer.software_genre Random forest Machine Learning Network Security Support vector machine Naive Bayes classifier Statistical classification Artificial Intelligence Control and Systems Engineering Artificial intelligence Feature Selection Electrical and Electronic Engineering DDoS business computer |
| Zdroj: | IAES International Journal of Artificial Intelligence (IJ-AI). 9:137 |
| ISSN: | 2252-8938 2089-4872 |
| DOI: | 10.11591/ijai.v9.i1.pp137-145 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|