Reference : Locust: Highly Concurrent DHT Experimentation Framework for Security Evaluations

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a journal
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Security, Reliability and Trust
	To cite this reference:	http://hdl.handle.net/10993/47657

Title :	Locust: Highly Concurrent DHT Experimentation Framework for Security Evaluations
Language :	English
Author, co-author :	Adamsky, Florian [Hof University of Applied Sciences]
	Kaiser, Daniel [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > PI Engel >]
	Steglich, Michael [Hof University of Applied Sciences]
	Engel, Thomas [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Publication date :	Nov-2020
Journal title :	Proceedings of ICPS ICCNS 2020
Peer reviewed :	Yes
Audience :	International
Event name :	ICCNS 2020: 10th International Conference on Communication and Network Security
Event date :	November 27 - 29, 2020
Event place (city) :	Tokyo
Event country :	Japan
Keywords :	[en] DHT ; Kademlia ; Security
Abstract :	[en] Distributed Hash Table (DHT) protocols, such as Kademlia, provide a decentralized key-value lookup which is nowadays integrated into a wide variety of applications, such as Ethereum, InterPlanetary File System (IPFS), and BitTorrent. However, many security issues in DHT protocols have not been solved yet. DHT networks are typically evaluated using mathematical models or simulations, often abstracting away from artefacts that can be relevant for security and/or performance. Experiments capturing these artefacts are typically run with too few nodes. In this paper, we provide Locust, a novel highly concurrent DHT experimentation framework written in Elixir, which is designed for security evaluations. This framework allows running experiments with a full DHT implementation and around 4,000 nodes on a single machine including an adjustable churn rate; thus yielding a favourable trade-off between the number of analysed nodes and being realistic. We evaluate our framework in terms of memory consumption, processing power, and network traffic.
Permalink :	http://hdl.handle.net/10993/47657
DOI :	10.1145/3442520.3442531
Other URL :	https://dl.acm.org/doi/10.1145/3442520.3442531

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