Comparison between genetic algorithm and self organizing map to detect botnet network traffic
Shinde Yugandhara Prabhakar,Pratishtha Parganiha,V. Madhu Viswanatham,M Nirmala +3 more
- Vol. 263, Iss: 4, pp 042103
TLDR
A comparative study is done on Genetic Algorithm and Self Organizing Map to detect the botnet network traffic and both are soft computing techniques used in this paper as data analytics system.Abstract:
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.read more
Citations
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Book ChapterDOI
Machine Learning Algorithms for Network Intrusion Detection
TL;DR: This chapter systematically reviews two groups of common intrusion detection systems using fuzzy logic and artificial neural networks, and evaluates them by utilizing the widely used KDD 99 benchmark dataset.
Book ChapterDOI
Bots a New Evolution of Robots: A Survey
TL;DR: This paper will try to explore different bots based on AI algorithms and try to know why the specific algorithms used in bots are different and also sketch a view of different types of bots in growing demand for automation.
References
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Proceedings ArticleDOI
A self-organizing map and its modeling for discovering malignant network traffic
TL;DR: Model-based intrusion detection and knowledge discovery are combined to cluster and classify P2P botnet traffic and other malignant network activity by using a Self-Organizing Map self-trained on denied Internet firewall log entries.
Proceedings ArticleDOI
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Proceedings ArticleDOI
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Proceedings ArticleDOI
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Proceedings ArticleDOI
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