Mobile botnet detection model based on retrospective pattern recognition
The dynamic nature of Botnets along with their sophisticated characteristics makes them one of the biggest threats to cyber security. Recently, the HTTP protocol is widely used by Botmaster as they can easily hide their command and control traffic amongst the benign web traffic. This paper proposes...
| Published in: | International Journal of Security and its Applications |
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| Main Author: | |
| Format: | Article |
| Language: | English |
| Published: |
Science and Engineering Research Support Society
2016
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992073868&doi=10.14257%2fijsia.2016.10.9.05&partnerID=40&md5=a3af90bfdfc2888cac26e2fc943f9c03 |
| Summary: | The dynamic nature of Botnets along with their sophisticated characteristics makes them one of the biggest threats to cyber security. Recently, the HTTP protocol is widely used by Botmaster as they can easily hide their command and control traffic amongst the benign web traffic. This paper proposes a Neural Network based model to detect mobile HTTP Botnets with random intervals independent of the packet payload, commands content, and encryption complexity of Bot communications. The experimental test results that were conducted on existing datasets and real world Bot samples show that the proposed method is able to detect mobile HTTP Botnets with high accuracy. © 2016 SERSC. |
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| ISSN: | 17389976 |
| DOI: | 10.14257/ijsia.2016.10.9.05 |