IoT Malware Dynamic Analysis Scheme Using the CNN Model

Jueun Jeon; Seungyeon Baek; Minho Kim; Inho Go; Young Sik Jeong

doi:10.1007/978-981-15-9343-7_77

IoT Malware Dynamic Analysis Scheme Using the CNN Model

Jueun Jeon
, Seungyeon Baek
, Minho Kim
, Inho Go
, Young Sik Jeong

Dongguk University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Recently, Internet of Things (IoT) technologies have been fused with next-generation technologies such as 5G and deep learning and used in diverse fields such as smart homes, smart cars, and smart appliances. As the demand for IoT devices increases, security threats targeting IoT devices, IoT infrastructure, and IoT application programs have also been increasing. Diverse studies on IoT malware detection have been conducted to protect IoT devices particularly from IoT malware among the security threats. However, existing studies can only accurately detect known IoT malware, not new and variant IoT malware. In this study, the malware dynamic analysis (MALDA) scheme that accurately detects new and variant malware that threatens IoT devices quickly is proposed to reduce the damage caused to IoT devices. The MALDA scheme dynamically analyzes IoT malware in nested cloud environments by training the behavioral features of IoT malware based on the Convolutional Neural Network (CNN) model.

Original language	English
Title of host publication	Advances in Computer Science and Ubiquitous Computing - CSA-CUTE 2019
Editors	James J. Park, Simon James Fong, Yi Pan, Yunsick Sung
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	547-553
Number of pages	7
ISBN (Print)	9789811593420
DOIs	https://doi.org/10.1007/978-981-15-9343-7_77
State	Published - 2021
Event	11th International Conference on Computer Science and its Applications, CSA 2019 and 14th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2019 - Macao, China Duration: 18 Dec 2019 → 20 Dec 2019

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	715
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	11th International Conference on Computer Science and its Applications, CSA 2019 and 14th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2019
Country/Territory	China
City	Macao
Period	18/12/19 → 20/12/19

Keywords

Deep learning
Dynamic analysis
Internet of things
Malware
Malware detection

Access to Document

10.1007/978-981-15-9343-7_77

Cite this

Jeon, J., Baek, S., Kim, M., Go, I., & Jeong, Y. S. (2021). IoT Malware Dynamic Analysis Scheme Using the CNN Model. In J. J. Park, S. J. Fong, Y. Pan, & Y. Sung (Eds.), Advances in Computer Science and Ubiquitous Computing - CSA-CUTE 2019 (pp. 547-553). (Lecture Notes in Electrical Engineering; Vol. 715). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-15-9343-7_77

@inproceedings{be941b99faad450a9dcae979af92929a,

title = "IoT Malware Dynamic Analysis Scheme Using the CNN Model",

abstract = "Recently, Internet of Things (IoT) technologies have been fused with next-generation technologies such as 5G and deep learning and used in diverse fields such as smart homes, smart cars, and smart appliances. As the demand for IoT devices increases, security threats targeting IoT devices, IoT infrastructure, and IoT application programs have also been increasing. Diverse studies on IoT malware detection have been conducted to protect IoT devices particularly from IoT malware among the security threats. However, existing studies can only accurately detect known IoT malware, not new and variant IoT malware. In this study, the malware dynamic analysis (MALDA) scheme that accurately detects new and variant malware that threatens IoT devices quickly is proposed to reduce the damage caused to IoT devices. The MALDA scheme dynamically analyzes IoT malware in nested cloud environments by training the behavioral features of IoT malware based on the Convolutional Neural Network (CNN) model.",

keywords = "Deep learning, Dynamic analysis, Internet of things, Malware, Malware detection",

author = "Jueun Jeon and Seungyeon Baek and Minho Kim and Inho Go and Jeong, \{Young Sik\}",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Singapore Pte Ltd.; 11th International Conference on Computer Science and its Applications, CSA 2019 and 14th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2019 ; Conference date: 18-12-2019 Through 20-12-2019",

year = "2021",

doi = "10.1007/978-981-15-9343-7\_77",

language = "English",

isbn = "9789811593420",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "547--553",

editor = "Park, \{James J.\} and Fong, \{Simon James\} and Yi Pan and Yunsick Sung",

booktitle = "Advances in Computer Science and Ubiquitous Computing - CSA-CUTE 2019",

address = "Germany",

}

Jeon, J, Baek, S, Kim, M, Go, I & Jeong, YS 2021, IoT Malware Dynamic Analysis Scheme Using the CNN Model. in JJ Park, SJ Fong, Y Pan & Y Sung (eds), Advances in Computer Science and Ubiquitous Computing - CSA-CUTE 2019. Lecture Notes in Electrical Engineering, vol. 715, Springer Science and Business Media Deutschland GmbH, pp. 547-553, 11th International Conference on Computer Science and its Applications, CSA 2019 and 14th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2019, Macao, China, 18/12/19. https://doi.org/10.1007/978-981-15-9343-7_77

IoT Malware Dynamic Analysis Scheme Using the CNN Model. / Jeon, Jueun; Baek, Seungyeon; Kim, Minho et al.
Advances in Computer Science and Ubiquitous Computing - CSA-CUTE 2019. ed. / James J. Park; Simon James Fong; Yi Pan; Yunsick Sung. Springer Science and Business Media Deutschland GmbH, 2021. p. 547-553 (Lecture Notes in Electrical Engineering; Vol. 715).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Baek, Seungyeon

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AU - Go, Inho

AU - Jeong, Young Sik

PY - 2021

Y1 - 2021

N2 - Recently, Internet of Things (IoT) technologies have been fused with next-generation technologies such as 5G and deep learning and used in diverse fields such as smart homes, smart cars, and smart appliances. As the demand for IoT devices increases, security threats targeting IoT devices, IoT infrastructure, and IoT application programs have also been increasing. Diverse studies on IoT malware detection have been conducted to protect IoT devices particularly from IoT malware among the security threats. However, existing studies can only accurately detect known IoT malware, not new and variant IoT malware. In this study, the malware dynamic analysis (MALDA) scheme that accurately detects new and variant malware that threatens IoT devices quickly is proposed to reduce the damage caused to IoT devices. The MALDA scheme dynamically analyzes IoT malware in nested cloud environments by training the behavioral features of IoT malware based on the Convolutional Neural Network (CNN) model.

AB - Recently, Internet of Things (IoT) technologies have been fused with next-generation technologies such as 5G and deep learning and used in diverse fields such as smart homes, smart cars, and smart appliances. As the demand for IoT devices increases, security threats targeting IoT devices, IoT infrastructure, and IoT application programs have also been increasing. Diverse studies on IoT malware detection have been conducted to protect IoT devices particularly from IoT malware among the security threats. However, existing studies can only accurately detect known IoT malware, not new and variant IoT malware. In this study, the malware dynamic analysis (MALDA) scheme that accurately detects new and variant malware that threatens IoT devices quickly is proposed to reduce the damage caused to IoT devices. The MALDA scheme dynamically analyzes IoT malware in nested cloud environments by training the behavioral features of IoT malware based on the Convolutional Neural Network (CNN) model.

KW - Deep learning

KW - Dynamic analysis

KW - Internet of things

KW - Malware

KW - Malware detection

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DO - 10.1007/978-981-15-9343-7_77

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Y2 - 18 December 2019 through 20 December 2019

ER -

IoT Malware Dynamic Analysis Scheme Using the CNN Model

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Keywords

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