Encryption and Compression Classification of Internet of Things Traffic
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| Název: | Encryption and Compression Classification of Internet of Things Traffic |
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| Autoři: | M Saleh, Mariam Najdat |
| Zdroj: | Browse all Theses and Dissertations |
| Informace o vydavateli: | CORE Scholar |
| Rok vydání: | 2023 |
| Sbírka: | Wright State University: CORE Scholar (Campus Online Repository) |
| Témata: | Department of Computer Science and Engineering, Physical Sciences and Mathematics, Computer Sciences, Engineering, Computer Engineering, Artificial Intelligence, Computer Science, Information Science, Information Technology, IoT, Encryption, Compression, Classification, Deep learning, Mish function, Entropy test, Chi-squared test, Anderson-Darling test, File Type, Inception Network, File Classification, Histogram, Kernel Density Estimation, Cumulative Density Function |
| Popis: | The Internet of Things (IoT) is used in many fields that generate sensitive data, such as healthcare and surveillance. Increased reliance on IoT raised serious information security concerns. This dissertation presents three systems for analyzing and classifying IoT traffic using Deep Learning (DL) models, and a large dataset is built for systems training and evaluation. The first system studies the effect of combining raw data and engineered features to optimize the classification of encrypted and compressed IoT traffic using Engineered Features Classification (EFC), Raw Data Classification (RDC), and combined Raw Data and Engineered Features Classification (RDEFC) approaches. Our results demonstrate that the EFC, RDC, and RDEFC models achieve a high classification accuracy of 80.94%, 86.45%, and 90.55%, respectively, outperforming systems reported in the literature with similar configurations. The second system uses three approaches of density estimation, which are histogram, Kernel Density Estimation (KDE), and Cumulative Distribution Function (CDF), to enhance encrypted and compressed variable-size IoT traffic classification. The results demonstrate that the KDE approach attains a significantly higher accuracy of 90.92% compared to 86.66% and 82.6% of the histogram and CDF, respectively. Furthermore, the KDE approach outperforms our RDEFC model in three aspects: variable file length, dataset complexity, and dimensionality reduction. The third system suggests a novel approach for file type classification of fragments in a compressed archive file for forensic digital investigation. Existing research in the literature classifies these files as archive file formats, such as .zip, with no further investigation of the compressed file types. In this system, an optimized modification of the Inception network is implemented. Two sets of filter sizes are implemented, and the attained accuracies are 73.18% and 75.24%, respectively. For future work, we suggest including more encryption or compression algorithms and ... |
| Druh dokumentu: | text |
| Popis souboru: | application/pdf |
| Jazyk: | unknown |
| Relation: | https://corescholar.libraries.wright.edu/etd_all/2819; https://corescholar.libraries.wright.edu/context/etd_all/article/3960/viewcontent/send.pdf |
| Dostupnost: | https://corescholar.libraries.wright.edu/etd_all/2819 https://corescholar.libraries.wright.edu/context/etd_all/article/3960/viewcontent/send.pdf |
| Rights: | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| Přístupové číslo: | edsbas.4348DE48 |
| Databáze: | BASE |
Nájsť tento článok vo Web of Science | Abstrakt: | The Internet of Things (IoT) is used in many fields that generate sensitive data, such as healthcare and surveillance. Increased reliance on IoT raised serious information security concerns. This dissertation presents three systems for analyzing and classifying IoT traffic using Deep Learning (DL) models, and a large dataset is built for systems training and evaluation. The first system studies the effect of combining raw data and engineered features to optimize the classification of encrypted and compressed IoT traffic using Engineered Features Classification (EFC), Raw Data Classification (RDC), and combined Raw Data and Engineered Features Classification (RDEFC) approaches. Our results demonstrate that the EFC, RDC, and RDEFC models achieve a high classification accuracy of 80.94%, 86.45%, and 90.55%, respectively, outperforming systems reported in the literature with similar configurations. The second system uses three approaches of density estimation, which are histogram, Kernel Density Estimation (KDE), and Cumulative Distribution Function (CDF), to enhance encrypted and compressed variable-size IoT traffic classification. The results demonstrate that the KDE approach attains a significantly higher accuracy of 90.92% compared to 86.66% and 82.6% of the histogram and CDF, respectively. Furthermore, the KDE approach outperforms our RDEFC model in three aspects: variable file length, dataset complexity, and dimensionality reduction. The third system suggests a novel approach for file type classification of fragments in a compressed archive file for forensic digital investigation. Existing research in the literature classifies these files as archive file formats, such as .zip, with no further investigation of the compressed file types. In this system, an optimized modification of the Inception network is implemented. Two sets of filter sizes are implemented, and the attained accuracies are 73.18% and 75.24%, respectively. For future work, we suggest including more encryption or compression algorithms and ... |
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