Öz
Providing machine learning (ML) based security in heterogeneous IoT networks including resource-constrained devices is a challenge because of the fact that conventional ML algorithms require heavy computations. Therefore, in this paper, lightweight ProtoNN, CMSIS-NN, and Bonsai tree ML algorithms were evaluated by using performance metrics such as testing accuracy, precision, F1 score and recall to test their classification ability on the IPv6 network dataset generated on resource-scarce embedded devices. The Bonsai tree algorithm provided the best performance results in all metrics (98.8 in accuracy, 98.9% in F1 score, 99.2% in precision, and 98.8% in recall) compared to the ProtoNN, and CMSIS-NN algorithms.
Anahtar Kelimeler
Embedded systems machine learning lightweight ML algorithms IPv6 Network cyber attack
Destekleyen Kurum
Recep Tayyip Erdogan University
Kaynakça
- Aamir, M., & Zaidi, S. M. A. (2021). Clustering-based semi-supervised machine learning for DDoS attack classification. Journal of King Saud University-Computer and Information Sciences, 33(4), 436-446.
- Alieksieiev, V., & Andrii, B. (2019, September). Information analysis and knowledge gain within graph data model. In 2019 IEEE 14th International Conference on Computer Sciences and Information Technologies (CSIT) (Vol. 3, pp. 268-271). IEEE.
- Beloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future generation computer systems, 28(5), 755-768.
- Ferrag, M. A., Maglaras, L., Moschoyiannis, S., & Janicke, H. (2020). Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study. Journal of Information Security and Applications, 50, 102419.
- Ge, M., Fu, X., Syed, N., Baig, Z., Teo, G., & Robles-Kelly, A. (2019, December). Deep learning-based intrusion detection for IoT networks. In 2019 IEEE 24th pacific rim international symposium on dependable computing (PRDC) (pp. 256-25609). IEEE.
- Gupta, C., Suggala, A. S., Goyal, A., Simhadri, H. V., Paranjape, B., Kumar, A., ... & Jain, P. (2017, July). Protonn: Compressed and accurate knn for resource-scarce devices. In International conference on machine learning (pp. 1331-1340). PMLR.
- Khandagale, S., Xiao, H., & Babbar, R. (2020). Bonsai: diverse and shallow trees for extreme multi-label classification. Machine Learning, 109(11), 2099-2119.
- Khraisat, A., Gondal, I., Vamplew, P., Kamruzzaman, J., & Alazab, A. (2019). A novel ensemble of hybrid intrusion detection system for detecting internet of things attacks. Electronics, 8(11), 1210.
- Lai, L., Suda, N., & Chandra, V. (2018). Cmsis-nn: Efficient neural network kernels for arm cortex-m cpus. arXiv preprint arXiv:1801.06601.
- Lamping, U., & Warnicke, E. (2004). Wireshark user's guide. Interface, 4(6), 1.
- Lonea, A. M., Popescu, D. E., & Tianfield, H. (2012). Detecting DDoS attacks in cloud computing environment. International Journal of Computers Communications & Control, 8(1), 70-78.
- SaiSindhuTheja, R., & Shyam, G. K. (2021). An efficient metaheuristic algorithm-based feature selection and recurrent neural network for DoS attack detection in cloud computing environment. Applied Soft Computing, 100, 106997.
- Sakr, F., Bellotti, F., Berta, R., De Gloria, A., & Doyle, J. (2021). Memory-Efficient CMSIS-NN with Replacement Strategy. In 2021 8th International Conference on Future Internet of Things and Cloud (FiCloud) (pp. 299-303). IEEE.
- Tekerek, A. (2021). A novel architecture for web-based attack detection using convolutional neural network. Computers & Security, 100, 102096.
- Tertytchny, G., Nicolaou, N., & Michael, M. K. (2020). Classifying network abnormalities into faults and attacks in IoT-based cyber physical systems using machine learning. Microprocessors and Microsystems, 77, 103121.
- Tuor, T., Wang, S., Salonidis, T., Ko, B. J., & Leung, K. K. (2018, April). Demo abstract: Distributed machine learning at resource-limited edge nodes. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (pp. 1-2). IEEE.
- Volkov, S. S., & Kurochkin, I. I. (2020). Network attacks classification using Long Short-term memory based neural networks in Software-Defined Networks. Procedia Computer Science, 178, 394-403.
- Yang, T. J., Chen, Y. H., Emer, J., & Sze, V. (2017, October). A method to estimate the energy consumption of deep neural networks. In 2017 51st asilomar conference on signals, systems, and computers (pp. 1916-1920). IEEE.
Öz
Providing machine learning (ML) based security in heterogeneous IoT networks including resource-constrained devices is a challenge because of the fact that conventional ML algorithms require heavy computations. Therefore, in this paper, lightweight ProtoNN, CMSIS-NN, and Bonsai tree ML algorithms were evaluated by using performance metrics such as testing accuracy, precision, F1 score and recall to test their classification ability on the IPv6 network dataset generated on resource-scarce embedded devices. The Bonsai tree algorithm provided the best performance results in all metrics (98.8 in accuracy, 98.9% in F1 score, 99.2% in precision, and 98.8% in recall) compared to the ProtoNN, and CMSIS-NN algorithms.
Anahtar Kelimeler
Embedded systems machine learning lightweight ML algorithms IPv6 Network cyber attack
Kaynakça
- Aamir, M., & Zaidi, S. M. A. (2021). Clustering-based semi-supervised machine learning for DDoS attack classification. Journal of King Saud University-Computer and Information Sciences, 33(4), 436-446.
- Alieksieiev, V., & Andrii, B. (2019, September). Information analysis and knowledge gain within graph data model. In 2019 IEEE 14th International Conference on Computer Sciences and Information Technologies (CSIT) (Vol. 3, pp. 268-271). IEEE.
- Beloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future generation computer systems, 28(5), 755-768.
- Ferrag, M. A., Maglaras, L., Moschoyiannis, S., & Janicke, H. (2020). Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study. Journal of Information Security and Applications, 50, 102419.
- Ge, M., Fu, X., Syed, N., Baig, Z., Teo, G., & Robles-Kelly, A. (2019, December). Deep learning-based intrusion detection for IoT networks. In 2019 IEEE 24th pacific rim international symposium on dependable computing (PRDC) (pp. 256-25609). IEEE.
- Gupta, C., Suggala, A. S., Goyal, A., Simhadri, H. V., Paranjape, B., Kumar, A., ... & Jain, P. (2017, July). Protonn: Compressed and accurate knn for resource-scarce devices. In International conference on machine learning (pp. 1331-1340). PMLR.
- Khandagale, S., Xiao, H., & Babbar, R. (2020). Bonsai: diverse and shallow trees for extreme multi-label classification. Machine Learning, 109(11), 2099-2119.
- Khraisat, A., Gondal, I., Vamplew, P., Kamruzzaman, J., & Alazab, A. (2019). A novel ensemble of hybrid intrusion detection system for detecting internet of things attacks. Electronics, 8(11), 1210.
- Lai, L., Suda, N., & Chandra, V. (2018). Cmsis-nn: Efficient neural network kernels for arm cortex-m cpus. arXiv preprint arXiv:1801.06601.
- Lamping, U., & Warnicke, E. (2004). Wireshark user's guide. Interface, 4(6), 1.
- Lonea, A. M., Popescu, D. E., & Tianfield, H. (2012). Detecting DDoS attacks in cloud computing environment. International Journal of Computers Communications & Control, 8(1), 70-78.
- SaiSindhuTheja, R., & Shyam, G. K. (2021). An efficient metaheuristic algorithm-based feature selection and recurrent neural network for DoS attack detection in cloud computing environment. Applied Soft Computing, 100, 106997.
- Sakr, F., Bellotti, F., Berta, R., De Gloria, A., & Doyle, J. (2021). Memory-Efficient CMSIS-NN with Replacement Strategy. In 2021 8th International Conference on Future Internet of Things and Cloud (FiCloud) (pp. 299-303). IEEE.
- Tekerek, A. (2021). A novel architecture for web-based attack detection using convolutional neural network. Computers & Security, 100, 102096.
- Tertytchny, G., Nicolaou, N., & Michael, M. K. (2020). Classifying network abnormalities into faults and attacks in IoT-based cyber physical systems using machine learning. Microprocessors and Microsystems, 77, 103121.
- Tuor, T., Wang, S., Salonidis, T., Ko, B. J., & Leung, K. K. (2018, April). Demo abstract: Distributed machine learning at resource-limited edge nodes. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (pp. 1-2). IEEE.
- Volkov, S. S., & Kurochkin, I. I. (2020). Network attacks classification using Long Short-term memory based neural networks in Software-Defined Networks. Procedia Computer Science, 178, 394-403.
- Yang, T. J., Chen, Y. H., Emer, J., & Sze, V. (2017, October). A method to estimate the energy consumption of deep neural networks. In 2017 51st asilomar conference on signals, systems, and computers (pp. 1916-1920). IEEE.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Yapay Zeka |
| Bölüm | PAPERS |
| Yazarlar | |
| Yayımlanma Tarihi | 10 Ekim 2022 |
| Gönderilme Tarihi | 8 Eylül 2022 |
| Kabul Tarihi | 16 Eylül 2022 |
| Yayımlandığı Sayı | Yıl 2022 Cilt: IDAP-2022 : International Artificial Intelligence and Data Processing Symposium |
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