Adaptive Malicious Activity Detection in Mobile Edge Networks Using  Deep Neural Network

Cherlapally Swapna, B. Jai Prakash, J. Manikanta Sai Gopi Krishna, M. Ranjith Kumar, B.Karthik, MD Faizzan Ali

Adaptive Malicious Activity Detection in Mobile Edge Networks Using Deep Neural Network

Authors

Cherlapally Swapna, B. Jai Prakash, J. Manikanta Sai Gopi Krishna, M. Ranjith Kumar, B.Karthik, MD Faizzan Ali

Keywords:

Mobile Edge Computing, Malicious Activity Detection, Deep Neural Network, Machine Learning, Network Security, Intrusion Detection, Adaptive Security, Cybersecurity, Real-Time Threat Detection.

Abstract

Mobile Edge Computing (MEC) enhances the performance of mobile applications by enablingcomputation and data storage closer to end users. However, this distributed architecture alsointroduces new security vulnerabilities, making mobile edge networks increasingly susceptible tomalicious activities such as data breaches, denial-of-service attacks, and intrusion attempts. Traditional security mechanisms

References

. R. Braden, “Requirements for Internet hosts-communication layers,” RFC 1122, Internet Engineering Task Force, Tech. Rep., Oct. 1989.

Downloads

Published

2025-05-25

How to Cite

Cherlapally Swapna, B. Jai Prakash, J. Manikanta Sai Gopi Krishna, M. Ranjith Kumar, B.Karthik, MD Faizzan Ali. (2025). Adaptive Malicious Activity Detection in Mobile Edge Networks Using Deep Neural Network. Journal of Computational Analysis and Applications (JoCAAA), 34(5), 506–515. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/4628