Visible to the public White-Hat Worm Launcher Based on Deep Learning in Botnet Defense System

TitleWhite-Hat Worm Launcher Based on Deep Learning in Botnet Defense System
Publication TypeConference Paper
Year of Publication2020
AuthorsKamilin, M. H. B., Yamaguchi, S.
Conference Name2020 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia)
Date PublishedNov. 2020
ISBN Number978-1-7281-6164-8
KeywordsBDS, Botnet, botnet defense system, botnets, composability, computer network performance evaluation, computer network security, Data models, Deep Learning, Grippers, Internet of Things, invasive software, IoT, IoT system, learning (artificial intelligence), malicious botnets, Malware, Mathematical model, Metrics, pubcrawl, resilience, Resiliency, Training data, white-hat botnets, white-hat worm launcher, worms placement

This paper proposes a deep learning-based white-hat worm launcher in Botnet Defense System (BDS). BDS uses white-hat botnets to defend an IoT system against malicious botnets. White-hat worm launcher literally launches white-hat worms to create white-hat botnets according to the strategy decided by BDS. The proposed launcher learns with deep learning where is the white-hat worms' right place to successfully drive out malicious botnets. Given a system situation invaded by malicious botnets, it predicts a worms' placement by the learning result and launches them. We confirmed the effect of the proposed launcher through simulating evaluation.

Citation Keykamilin_white-hat_2020