A Comprehensive Review of Vulnerabilities and Ai-Enabled Defense Against DDOS Attacks for Securing Cloud Services
DOI:
https://doi.org/10.32628/CSEIT251112355Keywords:
Internet of Things, Deep Learning, Intrusion Detection System, FPGA Acceleration, Software-Defined NetworkingAbstract
In this paper, it proposes a software defined end edge cloud architecture coupled with IP FPGA acceleration to integrate the two lines of checks, as well as a manner to use deep learning based IP FPGA to secure IoT networks. In this work, it uses the Caffe framework to implement multiple CNN architectures such as LeNet, AlexNet, and ResNet50, reaching detection accuracy rates of up to 97.8%. The performance improvement of such an engine on a Xilinx KU115 FPGA is shown by implementing it: 1000 packets per second, with low power consumption. It validates the results through a real use case of how this hybrid approach can be used for real time threat detection in large scale IoT deployments.
Downloads
References
Mylrea, M. and Gourisetti, S.N.G., 2017. Cybersecurity and optimization in smart “autonomous” buildings. Autonomy and Artificial Intelligence: A Threat or Savior?, pp.263-294.
Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Dafoe, A., Scharre, P., Zeitzoff, T., Filar, B. and Anderson, H., 2018. The malicious use of artificial intelligence: Forecasting, prevention, and mitigation. arXiv preprint arXiv:1802.07228.
Nsunza, W.W., Tetteh, A.Q.R. and Hei, X., 2018, October. Accelerating a secure programmable edge network system for smart classroom. In 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (pp. 1384-1389). IEEE.
Masombuka, M., Grobler, M. and Watson, B., 2018, June. Towards an artificial intelligence framework to actively defend cyberspace. In European Conference on Cyber Warfare and Security (pp. 589-XIII). Academic Conferences International Limited.
Shoaib, M., 2016. AI-enabled cyber weapons and implications for cybersecurity. Journal of Strategic Affairs of, pp.9-37.
Mylrea, M. and Gourisetti, S.N.G., 2017. Cybersecurity and optimization in smart “autonomous” buildings. Autonomy and Artificial Intelligence: A Threat or Savior?, pp.263-294.
Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Dafoe, A., Scharre, P., Zeitzoff, T., Filar, B. and Anderson, H., 2018. The malicious use of artificial intelligence: Forecasting, prevention, and mitigation. arXiv preprint arXiv:1802.07228.
Rehaman, F., 2018. Hidden Threat Detection in Big Data: Exploring the Role of Supervised Classifiers in AI and Machine Learning Technology.
Destré, E., 2018. Risks and Advantages in Using Artificial Intelligence on Cyber Defence and Cyber Attack. In Cyber Defence in Industry 4.0 Systems and Related Logistics and IT Infrastructures (pp. 27-37). IOS Press.
Masombuka, M., 2018. Towards an artificial intelligence framework to actively defend cyberspace in South Africa (Doctoral dissertation, Stellenbosch: Stellenbosch University).
Nsunza, W.W., Tetteh, A.Q.R. and Hei, X., 2018, October. Accelerating a secure programmable edge network system for smart classroom. In 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (pp. 1384-1389). IEEE.
Ferrag, M.A., Maglaras, L., Argyriou, A., Kosmanos, D. and Janicke, H., 2018. Security for 4G and 5G cellular networks: A survey of existing authentication and privacy-preserving schemes. Journal of Network and Computer Applications, 101, pp.55-82.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.