A Novel Framework to Classify Malware in MIPS Architecture-Based IoT Devices

Phu, Tran Nghi; Dang, Kien Hoang; Quoc, Dung Ngo; Dai, Nguyen Tho; Bình, Nguyễn Ngọc

doi:10.1155/2019/4073940

Cited by 14 publications

(3 citation statements)

References 25 publications

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“…Second, even if we somehow obtain the firmware, emulation testing is difficult because there is no unified firmware analysis method due to the diversity of IoT device architectures (e.g., MIPS [31], RISC-V [32]). For instance, we have to set up various configurations such as firmware unpacking (or decryption) and NVRAM parameter settings [5] to set up the made-to-order emulation environments, using a lot of manual effort.…”

Section: Related Workmentioning

confidence: 99%

FuzzDocs: An Automated Security Evaluation Framework for IoT

You

Kim

et al. 2022

IEEE Access

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As Internet of Things (IoT) devices have rooted themselves in the daily life of billions of people, security threats targeting IoT devices are emerging rapidly. Thus, IoT vendors have employed security testing frameworks to examine IoT devices before releasing them. However, existing frameworks have difficulty providing automated testing, as they require a lot of manual effort to support new devices due to the lack of information about the input formats of the new devices. To address this challenge, we introduce FUZZDOCS, a document-based black-box IoT testing framework designed to automatically analyze publicly accessible API documents about target IoT devices and extract information, including valid inputs used to call each functionality of the target devices. Based on the extracted information, it generates valid-enough test inputs that are not easily rejected by target devices but can trigger vulnerabilities deep inside them. This document-based input generation allows FUZZDOCS to support new devices without manual work, as well as provide effective security testing. To prove its feasibility, we evaluated FUZZDOCS in a real-world IoT environment, and the results showed that FUZZDOCS extracted input formats with 93% accuracy from hundreds of pages of documents. Also, it outperformed the existing frameworks in testing coverage and found 35 potential vulnerabilities, including two unexpected system failures in five popular IoT devices.

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Section: Related Workmentioning

confidence: 99%

FuzzDocs: An Automated Security Evaluation Framework for IoT

You

Kim

et al. 2022

IEEE Access

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“…T. N. Phu et al [17] proposed a novel framework called IDMD (IoT Dynamic Malware Detection), for the detection of malicious MIPS ELF files. The samples collected included 3,223 malware samples (from IoTPoT and Detux) and 228 benign ELF files (from D-Link and TP-Link firmware).…”

Section: Studies That Use Dynamic Analysis Approachmentioning

confidence: 99%

Machine Learning-based Detection of IoT Malware using System Call Data

Tahir

Qadir

2022

Preprint

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The popularity of IoT has raised many security concerns. Multiple devices with new and improved features are introduced into the market every year, expanding the IoT attack surface. The number of emerging malware variants are also increasing along with the use of antidetection mechanisms such as packing and obfuscation. The cyber security community has proposed multiple solutions, but the platform heterogeneity of IoT devices poses a unique challenge. Most studies use a static analysis approach for malware detection. However, this approach is not effective for the detection of packed and obfuscated malware. A few studies use a dynamic analysis approach, but they do not address the multiarchitecture nature of IoT devices. The main contribution of this research is to present a machine learning model that can detect rapidly evolving, packed or obfuscated malware that targets MIPS, ARM, and x86 IoT devices. We compile our own dataset by dynamically analysing 524 malware samples and 524 benign samples for various IoT architectures. The extracted system call data is preprocessed and used to train five machine learning models. The best results are achieved using Random Forest (RF) with a detection accuracy of 99.04% and FPR of 0.01.

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“…Motivados por questões financeiras, mas também por razões políticas, ideológicas, e por protestos ou espionagem, o número de malwares em dispositivos IoT cresceu exponencialmente desde a última década. Por exemplo, no primeiro semestre de 2018 foram detectados pelo Kaspersky IoT Lab mais de cento e vinte mil instâncias de malwares IoT (Phu et al, 2019).…”

Section: Introductionunclassified

Vulnerabilidades de Segurança da Informação na Indústria 4.0: Proposição de Critérios para o uso de Análise Multicritério

Sotolani¹,

Menezes²,

Galegale³

et al. 2022

Exacta

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Este artigo tem como objetivo identificar os critérios encontrados na literatura a serem utilizados em um método de análise multicritério para a priorização no tratamento de vulnerabilidades de segurança da informação na indústria 4.0. O progresso da Indústria 4.0 tem relevância cada vez maior considerando o aumento das vulnerabilidades de segurança e o aumento da complexidade em priorizá-las e tomar decisões. Um método como o Analytic Hierarchy Process (AHP) é uma proposta de solução. A metodologia utilizada foi a pesquisa bibliométrica nas bases SCOPUS e Web of Science. O resultado identificou oito critérios e 34 subcritérios relacionados ao tratamento das vulnerabilidades de segurança da informação da indústria 4.0 para aplicação de um método como o AHP. Os resultados permitem conduzir novas pesquisas através de outros métodos como entrevistas, survey e aplicação prática do método AHP aos critérios encontrados auxiliando a tomada de decisão.

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A Novel Framework to Classify Malware in MIPS Architecture-Based IoT Devices

Cited by 14 publications

References 25 publications

FuzzDocs: An Automated Security Evaluation Framework for IoT

FuzzDocs: An Automated Security Evaluation Framework for IoT

Machine Learning-based Detection of IoT Malware using System Call Data

Vulnerabilidades de Segurança da Informação na Indústria 4.0: Proposição de Critérios para o uso de Análise Multicritério

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