An Empirical Study on Darknet Visualization Based on Topological Data Analysis

DOI

10.2991/ijndc.k.201231.001How to use a DOI?

Keywords

Darknet monitoring; topological data analysis; clustering; visualization

Abstract

We are experiencing the true dawn of an Internet of Things society, in which all things are connected to the Internet. While this enables us to receive a wide variety of useful services via the Internet, we cannot ignore the fact that this means the number of devices targeted for Internet attacks has also increased. One known method for handling such issues is the utilization of a darknet monitoring system, which urgently provides information on attack trends occurring on the Internet. This system monitors and analyzes malicious packets in the unused IP address space and provides security related information to both network administrators and ordinary users. In this paper, Topological Data Analysis (TDA) Mapper is utilized to analyze malicious packets on the darknet, which grow increasingly complexity every day from a new perspective. TDA Mapper is a method of TDA that has continued to attract attention in recent years. In an evaluation experiment, by applying TDA to malicious packets monitored using the actual darknet, the malicious packets were able to be visualized. In this study, the author considers the overall image of the visualized malicious packets and examples extracted from the relationships among packets and reports on the effectiveness of the proposed method.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Masaki Narita
PY  - 2021
DA  - 2021/01/13
TI  - An Empirical Study on Darknet Visualization Based on Topological Data Analysis
JO  - International Journal of Networked and Distributed Computing
SP  - 52
EP  - 58
VL  - 9
IS  - 1
SN  - 2211-7946
UR  - https://doi.org/10.2991/ijndc.k.201231.001
DO  - 10.2991/ijndc.k.201231.001
ID  - Narita2021
ER  -