Abstract
As increasing the size of network, the malware propagates to other network easily. Moreover, malware is hard to detect if it is modified. The complexity of current network also causes the weakness for malware detection. Therefore, SDN quarantine network architecture has been researched. We applied the improved PE-miner framework that is malware detection mechanism based on machine learning algorithm to the SQN 1st quarantine. 1st quarantine is the system that filtering the malware using static mechanism. In this paper, detection rate of the improved PE-miner framework was evaluated and the real-time performance was also tested. Referring the result, we have proved that applying the PE-miner framework to SQN 1st quarantine is permissible.
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Acknowledgments
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. R-20160222-002755, Cloud based Security Intelligence Technology Development for the Customized Security Service Provisioning). And also this research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2010-0020210).
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Kim, DH., Lee, SH., Doo, WS., Ahn, SI., Chung, TM. (2017). Applying PE-Miner Framework to Software Defined Network Quarantine. In: Park, J., Pan, Y., Yi, G., Loia, V. (eds) Advances in Computer Science and Ubiquitous Computing. UCAWSN CUTE CSA 2016 2016 2016. Lecture Notes in Electrical Engineering, vol 421. Springer, Singapore. https://doi.org/10.1007/978-981-10-3023-9_89
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DOI: https://doi.org/10.1007/978-981-10-3023-9_89
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