A Cross-Platform Study on IoT Malware

Ban, Tao; Isawa, Ryoichi; Yoshioka, Katsunari; Inoue, Daisuke

doi:10.23919/icmu.2018.8653580

Cited by 5 publications

(9 citation statements)

References 3 publications

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“…A sample is excluded from the analysis if its API/opcode sequence is shorter than 10, indicating highly possibility of a cracked file. This makes the dataset slightly smaller than that in [4].…”

Section: Datamentioning

confidence: 99%

“…We use the same dataset introduced in [4] for the analysis. It consists of 9,085 IoT malware samples collected from July 8, 2017 to January 20, 2018 using IoTPOT.…”

Section: Datamentioning

confidence: 99%

“…Following the same procedure, we also generate API features for each malicious program from the API-call trace logged by the strace command during its execution in a virtualized sandbox. Compared with the Vector Space Model approach adopted in [4], the n-gram representation preserves the order information in the sequence and thus may lead to superior generalization performance. In the n-gram representation, each sample is coded as a list of n-grams, which tends to reside in very high dimensional features space.…”

Section: Feature Engineeringmentioning

confidence: 99%

See 2 more Smart Citations

A Cross-Platform Study on Emerging Malicious Programs Targeting IoT Devices

Ban

Isawa

Huang

et al. 2019

IEICE Trans. Inf. & Syst.

Self Cite

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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.

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Section: Datamentioning

confidence: 99%

“…We use the same dataset introduced in [4] for the analysis. It consists of 9,085 IoT malware samples collected from July 8, 2017 to January 20, 2018 using IoTPOT.…”

Section: Datamentioning

confidence: 99%

Section: Feature Engineeringmentioning

confidence: 99%

See 1 more Smart Citation

A Cross-Platform Study on Emerging Malicious Programs Targeting IoT Devices

Ban

Isawa

Huang

et al. 2019

IEICE Trans. Inf. & Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The static analysis relies on extracting certain characteristics: Control Flow Graph (CFG), Function Call Graph (FCG), opcodes, strings, and file headers. Then, the assembly code is disassembled [32] using tools like Radare2 [33] and IDA Pro [34]. These characteristic features can be categorized as graph-based features and non-graph-based features.…”

Section: ) Static Analysismentioning

confidence: 99%

IoT Malware Analysis Using Federated Learning: A Comprehensive Survey

2023

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The Internet of Things (IoT) has paved the way to a highly connected society where all things are interconnected and exchanging information has become more accessible through the internet. With the use of IoT devices, the threat of malware has increased rapidly. The increased number of existing and new malware variants has made protecting IoT devices and networks challenging. The malware can hide in the systems and disables its activity when there are attempts to discover and detect them. With technological advances, there are various emerging techniques to address this problem. However, they still encounter issues concerning the privacy and security of the user's data and suffer from a single point of failure. To address this issue, there are recent research developments conducted to use Federated Learning (FL). FL is a decentralized technique that trains the user's data on-device and exchanges the parameters without sharing the user's data. FL is implemented to secure the user's data, provide safe and accurate models, and prevent the single point of failure in the centralized models. This paper provides an overview of different approaches that integrate FL with IoT. Finally, we discuss the applications of FL, the research challenges, and future research directions.

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“…However, because IoT devices are composed of more than ten architectures, as shown in Tab. 1, it is difficult to apply a classification method using executed code analysis in a conventional Windows or mobile environment [2,3]. Hence, a separate analysis process is applied according to the instruction set architecture (ISA).…”

Section: Introductionmentioning

confidence: 99%

Massive IoT Malware Classification Method Using Binary Lifting

Jeong¹,

Kwak²

2022

Intelligent Automation &Amp; Soft Computing

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Owing to the development of next-generation network and data processing technologies, massive Internet of Things (IoT) devices are becoming hyperconnected. As a result, Linux malware is being created to attack such hyperconnected networks by exploiting security threats in IoT devices. To determine the potential threats of such Linux malware and respond effectively, malware classification through an analysis of the executed code is required; however, a limitation exists in that each heterogeneous architecture must be analyzed separately. However, the binary codes of a heterogeneous architecture can be translated to a high-level intermediate representation (IR) of the same format using binary lifting and malicious behavior information can be identified because the functions and parameters of the assembly code are stored in the IR. Consequently, this study suggests a Linux malware classification method applicable to various architectures by converting Linux assembly codes into an IR using binary lifting and then learning the IR Sequence which reflects malicious behavior pattern using deep learning model for sequence learning.

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A Cross-Platform Study on IoT Malware

Cited by 5 publications

References 3 publications

A Cross-Platform Study on Emerging Malicious Programs Targeting IoT Devices

A Cross-Platform Study on Emerging Malicious Programs Targeting IoT Devices

IoT Malware Analysis Using Federated Learning: A Comprehensive Survey

Massive IoT Malware Classification Method Using Binary Lifting

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