Scalable and Fast Algorithm for Constructing Phylogenetic Trees With Application to IoT Malware Clustering
Author:
Affiliation:
1. Graduate School and Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan
2. National Institute of Information and Communications Technology, Koganei, Japan
Funder
Ministry of Internal Affairs and Communications, Japan, through the “MITIGATE” among “Research and Development for Expansion of Radio Wave Resources”
JST SPRING
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10024280.pdf?arnumber=10024280
Reference35 articles.
1. Scalable and Fast Hierarchical Clustering of IoT Malware Using Active Data Selection
2. N-gram MalGAN: Evading machine learning detection via feature n-gram
3. A Fast Algorithm for Constructing Phylogenetic Trees with Application to IoT Malware Clustering
4. Detecting overlapping protein complexes in dynamic protein-protein interaction networks by developing a fuzzy clustering algorithm
5. MutantX-S: Scalable malware clustering based on static features;hu;Proc USENIX Annu Tech Conf,2013
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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. Work in Progress;Proceedings of the 20th ACM International Conference on Computing Frontiers;2023-05-09
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