Comparison of Performance Between Incremental and Batch Learning Method for Information Analysis of Cyber Surveillance and Reconnaissance


KIPS Transactions on Software and Data Engineering, Vol. 7, No. 3, pp. 99-106, Mar. 2018
10.3745/KTSDE.2018.7.3.99,   PDF Download:
Keywords: Cyber ISR, Incremental Learning Method, Batch Learning Method
Abstract

In the process of acquiring information through the cyber ISR (Intelligence Surveillance Reconnaissance) and research into the agent to help decision-making, periodic communication between the C&C (Command and Control) server and the agent may not be possible. In this case, we have studied how to effectively surveillance and reconnaissance. Due to the network configuration, agents planted on infiltrated computers can not communicate seamlessly with C&C servers. In this case, the agent continues to collect data continuously, and in order to analyze the collected data within a short time in When communication is possible with the C&C server, it can utilize limited resources and time to continue its mission without being discovered. This research shows the superiority of incremental learning method over batch method through experiments. At an experiment with the restricted memory of 500 mega bytes, incremental learning method shows 10 times decrease in learning time. But at an experiment with the reuse of incorrectly classified data, the required time for relearn takes twice more.


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Cite this article
[IEEE Style]
G. Shin, H. Yooun, D. Shin, D. Shin, "Comparison of Performance Between Incremental and Batch Learning Method for Information Analysis of Cyber Surveillance and Reconnaissance," KIPS Transactions on Software and Data Engineering, vol. 7, no. 3, pp. 99-106, 2018. DOI: 10.3745/KTSDE.2018.7.3.99.

[ACM Style]
Gyeong-Il Shin, Hosang Yooun, DongIl Shin, and DongKyoo Shin. 2018. Comparison of Performance Between Incremental and Batch Learning Method for Information Analysis of Cyber Surveillance and Reconnaissance. KIPS Transactions on Software and Data Engineering, 7, 3, (2018), 99-106. DOI: 10.3745/KTSDE.2018.7.3.99.