Abstract
With the development of IT technology, cyber intelligence, surveillance, and reconnaissance (ISR) have become more important than traditional military ISR. In this paper, we propose an effective method to efficiently operate cyber ISR using machine learning, especially utilizing incremental learning methods. We compare two learning methods that are suitable for use in the self-learning agent model of self-survival in a closed network. The result shows that the incremental learning method reduces the memory needed by more than 20%, with an accuracy of more than 82%. Thus, the incremental learning method can be very effective in a closed military network.
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This work was supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract(UD160066BD).
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Communicated by G. Yi.
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Shin, G., Yooun, H., Shin, D. et al. Incremental learning method for cyber intelligence, surveillance, and reconnaissance in closed military network using converged IT techniques. Soft Comput 22, 6835–6844 (2018). https://doi.org/10.1007/s00500-018-3433-1
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DOI: https://doi.org/10.1007/s00500-018-3433-1