Scalable and Fast Hierarchical Clustering of IoT Malware Using Active Data Selection-Reference-Cited by-同舟云学术

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Scalable and Fast Hierarchical Clustering of IoT Malware Using Active Data Selection

Published:2021-12-06 Issue: Volume: Page:
ISSN:
Container-title:2021 Sixth International Conference on Fog and Mobile Edge Computing (FMEC)
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Author:

He Tianxiang¹,Han Chansu²,Takahashi Takeshi²,Kijima Shuji¹,Takeuchi Jun'ichi¹

Affiliation:

1. Kyushu University,Fukuoka,Japan

2. National Institute of Information and Communications Technology,Tokyo,Japan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9732403/9732404/09732550.pdf?arnumber=9732550

Reference21 articles.

1. Inves-tigating behavioral differences between IoT mal ware via function call sequence graphs;kawasoe;In Proceedings of the 36th Annual ACM Symposium on Applied Computing,0

2. The Similarity Metric

3. Applied Statistical Decision Theory.

4. HAC-T and Fast Search for Similarity in Security

5. Insecurity by Design: Today's IoT Device Security Problem

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Towards Functional Analysis of IoT Malware Using Function Call Sequence Graphs and Clustering;2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC);2023-06

2. Scalable and Fast Algorithm for Constructing Phylogenetic Trees With Application to IoT Malware Clustering;IEEE Access;2023

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