IOT TIZIMLARIDA KIBERHUJUMLARNI ANIQLASH: MASHINALI O‘RGANISHGA ASOSLANGAN XAVFSIZLIK ARXITEKTURASI
Keywords:
Kalit so‘zlar: Buyumlar interneti (IoT), kiberxavfsizlik, mashinali o‘rganish, anomaliyalarni aniqlash, tarmoq trafigi, DDoS hujumlari, chekka hisoblash (Edge Computing).Abstract
Annotatsiya. Ushbu maqolada Buyumlar interneti (IoT) ekotizimlarida xavfsizlikni ta’minlashning dolzarb muammolari tadqiq etiladi. Maqolaning maqsadi - IoT qurilmalariga xos bo‘lgan cheklangan hisoblash resurslari sharoitida kiberhujumlarni (DDoS, Botnet, Brute-force) real vaqt rejimida aniqlash uchun mashinali o‘rganish algoritmlarini qo‘llash samaradorligini tahlil qilishdir. Tadqiqot jarayonida turli xil anomaliyalarni aniqlash modellari va ularning tarmoq trafigini tahlil qilishdagi o‘rni injiniring nuqtai nazaridan yoritilgan.
References
1. O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in Medical Image Computing and Computer-Assisted Intervention MICCAI 2015, Cham: Springer International Publishing, 2015, pp. 234–241.
2. F. Milletari, N. Navab, and S. A. Ahmadi, “V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation,” in 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA, 2016, pp. 565–571. doi: 10.1109/3DV.2016.79.
3. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016. [Online]. Available: http://www.deeplearningbook.org
4. Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang, “UNet: A Nested U-Net Architecture for Medical Image Segmentation,”in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Cham: Springer, 2018, pp. 3–11.
5. A. Paszke et al., “ PyTorch: An Imperative Style, High-Performance Deep Learning Library, ” in Advances in Neural Information Processing Systems 32, 2019, pp. 8024–8035.
