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- W2994804501 abstract "Abstract Threats emanating from Internet of Things (IoT) malicious software are fast evolving and complex phenomena. Unlike conventional networks, Internet of things have unique attributes such as heterogeneity of devices, high scalability and diverse architectures that makes its malware analysis complex. In this paper, we propose an approach for analyzing and classifying IoT malware using Haralick image texture features and machine learning classifiers namely K-nearest neighbor(KNN), naive Bayes (NB) and random forest(RF). A binary file (malicious or benign) is converted to a gray scale image . The gray level co-occurence matrix (GLCM) is computed on each of the extracted image. On the basis of these GLCM parameters, five Haralick features namely angular second moment, entropy, contrast, inverse different moment and correlation are calculated. Finally, these Haralick texture features are used to perform malware classification using random forest, naive Bayes and K-nearest neighbor. Experimental results shows that our approach obtains 95% accuracy for Random Forest, 89% for naive Bayes and 80% for K-nearest neighbor classifiers . Overall, use of texture feature can realize a low computational and platform independent classification scheme for IoT malware." @default.
- W2994804501 created "2019-12-26" @default.
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- W2994804501 date "2020-03-01" @default.
- W2994804501 modified "2023-09-27" @default.
- W2994804501 title "Analysis of internet of things malware using image texture features and machine learning techniques" @default.
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- W2994804501 doi "https://doi.org/10.1016/j.iot.2019.100153" @default.
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