IoT malware classification based on reinterpreted function-call graphs

Wu, Chia‐Yi; Ban, Tao; Cheng, Shin‐Ming; Takahashi, Takeshi; Inoue, Daisuke

doi:10.1016/j.cose.2022.103060

Cited by 10 publications

(2 citation statements)

References 21 publications

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“…The first method converts the permission features into gray images, converting the malware detection task into an image classification task with CNN model 29 . The second method is based on API calls' frequency and importance, ignoring the APK's permissions 44 . The third method is based on the n‐gram of opcodes and uses the frequency of triplet features to represent 54 .…”

Section: Experimental Analysismentioning

confidence: 99%

“…29 The second method is based on API calls' frequency and importance, ignoring the APK's permissions. 44 The third method is based on the n-gram of opcodes and uses the frequency of triplet features to represent. 54 The paper compares the detection results using different malware features and deep learning methods, as shown in Table 6 below.…”

Section: Experimental Evaluationmentioning

confidence: 99%

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MUDROID: Android malware detection and classification based on permission and behavior for autonomous vehicles

Tang,

Da,

Wang

et al. 2023

Trans Emerging Tel Tech

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With the growing popularity of autonomous vehicles, ensuring their safety has become a significant concern. Vehicle manufacturers have incorporated the Android operating system to enhance user convenience. However, the Android operating system's diversity and vulnerability may significantly impact autonomous vehicles because these natures lead to a critical threat in autonomous vehicle security: Android malware. The complex nature of multi‐data sources fusion in autonomous driving systems has resulted in low detection efficiency and accuracy for Android malware. Given the security requirements for autonomous‐driving systems, this article presents an Android malware classification method centered around the multifeature fusion and deep learning architecture designed to detect and classify Android malware within autonomous‐driving systems. The proposed MUDROID framework relies on the Android permission mechanism and behavioral feature to formulate a malware classification model. Comprising four essential components—feature extraction, representation, multifeature fusion, and classifier training, MUDROID leverages the advantages of CNN and GCN combination model and is adept at extracting and interpreting multidimensional features, consequently constructing a multifeature fusion classifier to amplify the efficiency and accuracy of Android malware detection and classification. The experiment results support MUDROID's superior classification performance, with an impressive Android malware detection accuracy rate of 98.69% and family classification (95.42%). The proposed method can detect and classify Android malware effectively, thus elevating the security and robustness of the self‐driving system. Additionally, the approach in this article also addresses feature representation issues in detecting malware variants, facilitating the recognition of Android malware variants across diverse platforms.

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Section: Experimental Analysismentioning

confidence: 99%