KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Hybrid Feature Selection Method Based on a Naïve Bayes Algorithm that Enhances the Learning Speed while Maintaining a Similar Error Rate in Cyber ISR

  • Shin, GyeongIl (Department of Computer Engineering, Se-jong University) ;
  • Yooun, Hosang (Agency for Defense Development) ;
  • Shin, DongIl (Department of Computer Engineering, Se-jong University) ;
  • Shin, DongKyoo (Department of Computer Engineering, Se-jong University)
  • Received : 2018.02.27
  • Accepted : 2018.08.27
  • Published : 2018.12.31
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Abstract

Cyber intelligence, surveillance, and reconnaissance (ISR) has become more important than traditional military ISR. An agent used in cyber ISR resides in an enemy's networks and continually collects valuable information. Thus, this agent should be able to determine what is, and is not, useful in a short amount of time. Moreover, the agent should maintain a classification rate that is high enough to select useful data from the enemy's network. Traditional feature selection algorithms cannot comply with these requirements. Consequently, in this paper, we propose an effective hybrid feature selection method derived from the filter and wrapper methods. We illustrate the design of the proposed model and the experimental results of the performance comparison between the proposed model and the existing model.

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References

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