Neural Network-Powered Intrusion Detection in Multi-Cloud and Fog Environments

Abstract: Cloud Computing has revolutionized the technological landscape, offering a platform for resource provisioning where organizations can access computing resources, storage, applications, and services. The shared nature of these resources introduces complexities in ensuring security and privacy. With the advent of edge and fog computing alongside cloud technologies, the processing, data storage, and management paradigm faces challenges in safeguarding against potential intrusions. Attacks on fog computing, IoT cloud, and related advancements can have pervasive and detrimental consequences. To address these concerns, various security standards and schemes have been suggested and deployed to enhance fog computing security. In particular, the focus of these security measures has become vital due to the involvement of multiple networks and numerous fog nodes through which end-users interact. These nodes facilitate the transfer of sensitive information, amplifying privacy concerns. This paper proposes a multi-layered intermittent neural network model tailored specifically for enhancing security in fog computing, especially in proximity to end-users and IoT devices. Emphasizing the need to mitigate privacy risks inherent in extensive network connections, the model leverages a customized adaptation of the NSLKDD dataset, a challenging dataset commonly applied to evaluate intrusion detection systems. A range of current models and feature sets are rigorously investigated to quantify the effectiveness of the proposed approach. Through comprehensive research findings and replication studies, the paper demonstrates the stability and robustness of the suggested method versus various performance metrics employed for intrusion detection. The assessment illustrates the model's superior capability in addressing privacy and security challenges in hybrid cloud environments incorporating intrusion detection systems, offering a promising solution for the evolving landscape of cloud-based computing technologies.

Yanfeng ZHANG and Zhe XU. “Neural Network-Powered Intrusion Detection in Multi-Cloud and Fog Environments”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.6 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150625

@article{ZHANG2024,
title = {Neural Network-Powered Intrusion Detection in Multi-Cloud and Fog Environments},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150625},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150625},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {6},
author = {Yanfeng ZHANG and Zhe XU}
}