Botnet detection and classification system
Date of Publication
2011
Document Type
Bachelor's Thesis
Degree Name
Bachelor of Science in Computer Science
College
College of Computer Studies
Department/Unit
Computer Science
Thesis Adviser
Miguel N. Gomez
Abstract/Summary
Botnets have been an issue for the past several years. Botnets have multiple capabilities to take over single computers or large networks thus, making them more dangerous than any other malware scattered around the Internet. A sign of a botnet infection is using the connection to send or receive data. Clustering of data to identify botnet activity plays an important role in preparation for future data analysis. Botnets are identified base on their behavior that deviates from a normal network activity. A set of attributes correspond to the behavior, in which it is clustered and analyzed to determine the family of a particular bot however, not all attributes present in the datasets are relevant in determining the botnet family given its behavior. In this paper, several datasets of malicious activity with different selected attributes crucial in correctly clustering botnets to their respective families. The viability of the Self-Organizing Map algorithm to classify botnets is verified during the course of the study.
Abstract Format
html
Language
English
Format
Accession Number
TU14683
Shelf Location
Archives, The Learning Commons, 12F, Henry Sy Sr. Hall
Physical Description
1 v. (various foliations) ; 28 cm.
Recommended Citation
Aquino, M. P., Co, M. T., & Wong, B. A. (2011). Botnet detection and classification system. Retrieved from https://animorepository.dlsu.edu.ph/etd_bachelors/11858
