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Authors: Masataka Nakahara ; Norihiro Okui ; Yasuaki Kobayashi and Yutaka Miyake

Affiliation: KDDI Research, Inc., 2–1–15, Ohara, Fujimino-shi, Saitama, Japan

Keyword(s): IoT Security, Malware, Anomaly Detection, Machine Learning, White List.

Abstract: The number of cyber-attacks using IoT devices is increasing with the growth of IoT devices. Since the number of routes malware infection is increasing, it is necessary not only to prevent infection but also to take measures after infection. Therefore, high-performance detection techniques are required, but many existing technologies require large amounts of data and heavy processing. Then, there is a need for a system that can detect malware infection while reducing the processing load. Therefore, we have proposed an architecture for detecting malware traffic using flow data of packets instead of whole packet information. We performed the malware traffic detection on the proposed architecture by using machine learning algorithms focusing on the behavior of IoT devices, and could detect malware with some degree of accuracy. In this paper, in order to improve the accuracy, we propose a hybrid system using machine learning and the white list automatically generated using the rule of Man ufacturer Usage Description (MUD). The white list eliminates benign packets from the target of malware traffic detection, and it can decrease the false positive rate. We evaluate the performance of proposed method and show the effectiveness. (More)

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Paper citation in several formats:
Nakahara, M.; Okui, N.; Kobayashi, Y. and Miyake, Y. (2021). Malware Detection for IoT Devices using Automatically Generated White List and Isolation Forest. In Proceedings of the 6th International Conference on Internet of Things, Big Data and Security - IoTBDS; ISBN 978-989-758-504-3; ISSN 2184-4976, SciTePress, pages 38-47. DOI: 10.5220/0010394900380047

@conference{iotbds21,
author={Masataka Nakahara. and Norihiro Okui. and Yasuaki Kobayashi. and Yutaka Miyake.},
title={Malware Detection for IoT Devices using Automatically Generated White List and Isolation Forest},
booktitle={Proceedings of the 6th International Conference on Internet of Things, Big Data and Security - IoTBDS},
year={2021},
pages={38-47},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010394900380047},
isbn={978-989-758-504-3},
issn={2184-4976},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Internet of Things, Big Data and Security - IoTBDS
TI - Malware Detection for IoT Devices using Automatically Generated White List and Isolation Forest
SN - 978-989-758-504-3
IS - 2184-4976
AU - Nakahara, M.
AU - Okui, N.
AU - Kobayashi, Y.
AU - Miyake, Y.
PY - 2021
SP - 38
EP - 47
DO - 10.5220/0010394900380047
PB - SciTePress