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A Cross-Platform Study on Emerging Malicious Programs Targeting IoT Devices
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Summary :
Along with the proliferation of IoT (Internet of Things) devices, cyberattacks towards them are on the rise. In this paper, aiming at efficient precaution and mitigation of emerging IoT cyberthreats, we present a multimodal study on applying machine learning methods to characterize malicious programs which target multiple IoT platforms. Experiments show that opcode sequences obtained from static analysis and API sequences obtained by dynamic analysis provide sufficient discriminant information such that IoT malware can be classified with near optimal accuracy. Automated and accelerated identification and mitigation of new IoT cyberthreats can be enabled based on the findings reported in this study.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.9 pp.1683-1685
- Publication Date
- 2019/09/01
- Publicized
- 2019/06/21
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018OFL0007
- Type of Manuscript
- Special Section LETTER (Special Section on Log Data Usage Technology and Office Information Systems)
- Category
- Cybersecurity
Authors
Tao BAN
National Institute of Information and Communications Technology
Ryoichi ISAWA
National Institute of Information and Communications Technology
Shin-Ying HUANG
Institute for Information Industry
Katsunari YOSHIOKA
National Institute of Information and Communications Technology,Yokohama National University
Daisuke INOUE
National Institute of Information and Communications Technology
Keyword
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Tao BAN, Ryoichi ISAWA, Shin-Ying HUANG, Katsunari YOSHIOKA, Daisuke INOUE, "A Cross-Platform Study on Emerging Malicious Programs Targeting IoT Devices" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 9, pp. 1683-1685, September 2019, doi: 10.1587/transinf.2018OFL0007.
Abstract: Along with the proliferation of IoT (Internet of Things) devices, cyberattacks towards them are on the rise. In this paper, aiming at efficient precaution and mitigation of emerging IoT cyberthreats, we present a multimodal study on applying machine learning methods to characterize malicious programs which target multiple IoT platforms. Experiments show that opcode sequences obtained from static analysis and API sequences obtained by dynamic analysis provide sufficient discriminant information such that IoT malware can be classified with near optimal accuracy. Automated and accelerated identification and mitigation of new IoT cyberthreats can be enabled based on the findings reported in this study.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018OFL0007/_p
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@ARTICLE{e102-d_9_1683,
author={Tao BAN, Ryoichi ISAWA, Shin-Ying HUANG, Katsunari YOSHIOKA, Daisuke INOUE, },
journal={IEICE TRANSACTIONS on Information},
title={A Cross-Platform Study on Emerging Malicious Programs Targeting IoT Devices},
year={2019},
volume={E102-D},
number={9},
pages={1683-1685},
abstract={Along with the proliferation of IoT (Internet of Things) devices, cyberattacks towards them are on the rise. In this paper, aiming at efficient precaution and mitigation of emerging IoT cyberthreats, we present a multimodal study on applying machine learning methods to characterize malicious programs which target multiple IoT platforms. Experiments show that opcode sequences obtained from static analysis and API sequences obtained by dynamic analysis provide sufficient discriminant information such that IoT malware can be classified with near optimal accuracy. Automated and accelerated identification and mitigation of new IoT cyberthreats can be enabled based on the findings reported in this study.},
keywords={},
doi={10.1587/transinf.2018OFL0007},
ISSN={1745-1361},
month={September},}
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TY - JOUR
TI - A Cross-Platform Study on Emerging Malicious Programs Targeting IoT Devices
T2 - IEICE TRANSACTIONS on Information
SP - 1683
EP - 1685
AU - Tao BAN
AU - Ryoichi ISAWA
AU - Shin-Ying HUANG
AU - Katsunari YOSHIOKA
AU - Daisuke INOUE
PY - 2019
DO - 10.1587/transinf.2018OFL0007
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2019
AB - Along with the proliferation of IoT (Internet of Things) devices, cyberattacks towards them are on the rise. In this paper, aiming at efficient precaution and mitigation of emerging IoT cyberthreats, we present a multimodal study on applying machine learning methods to characterize malicious programs which target multiple IoT platforms. Experiments show that opcode sequences obtained from static analysis and API sequences obtained by dynamic analysis provide sufficient discriminant information such that IoT malware can be classified with near optimal accuracy. Automated and accelerated identification and mitigation of new IoT cyberthreats can be enabled based on the findings reported in this study.
ER -
English
