Comparison between genetic algorithm and self organizing map to detect botnet network traffic

14th International Conference on Science, Engineering and Technology

自然科学;工业技术

Prabhakar, Shinde Yugandhara^1 ; Parganiha, Pratishtha^1 ; Madhu Viswanatham, V.^1 ; Nirmala, M.^2

School of Computer Science and Engineering, VIT University, Vellore

Tamil Nadu

632014, India^1

School of Information Technology and Engineering, VIT University, Vellore

Tamil Nadu

632014, India^2

关键词: Comparative studies;    Cyber security;    Data analytics;    Malicious activities;    Natural evolution;    Network traffic;    Softcomputing techniques;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/263/4/042103/pdf
DOI  :  10.1088/1757-899X/263/4/042103

来源: IOP

【 摘 要 】

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.