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Comparison between genetic algorithm and self organizing map to detect botnet network traffic
Prabhakar S.Y, Parganiha P, ,
Published in IOP Publishing
Volume: 263
Issue: 4
In Cyber Security world the botnet attacks are increasing. To detect botnet is a challenging task. Botnet is a group of computers connected in a coordinated fashion to do malicious activities. Many techniques have been developed and used to detect and prevent botnet traffic and the attacks. In this paper, a comparative study is done on Genetic Algorithm (GA) and Self Organizing Map (SOM) to detect the botnet network traffic. Both are soft computing techniques and used in this paper as data analytics system. GA is based on natural evolution process and SOM is an Artificial Neural Network type, uses unsupervised learning techniques. SOM uses neurons and classifies the data according to the neurons. Sample of KDD99 dataset is used as input to GA and SOM. © Published under licence by IOP Publishing Ltd.
About the journal
JournalData powered by TypesetIOP Conference Series: Materials Science and Engineering
PublisherData powered by TypesetIOP Publishing
Open AccessYes