Projects per year
Abstract
The ever-expanding landscape of advanced applications and services, as well as the associated emerging attacks in the zero-touch network and service management (ZSM) paradigm, necessitates novel approaches to manage complex network infrastructures while addressing the security requirements of beyond 5G networks. To address this issue, we present a cutting-edge, novel semi-supervised federated learning approach that incorporates a Deep Reinforcement Learning (DRL) agent for real-time defense system updates. Specifically, we propose the DRL-FedUSS framework, which stands for DRL-based Federated Uncertainty-guided Semi-Supervised learning. DRL-FedUSS is designed explicitly for Label-at-Client scenarios to accelerate the training convergence when clients hold a scarcity of labeled and an abundance of unlabeled network traffic samples. The DRL-FedUSS framework integrates a DRL agent that intelligently selects the most informative samples with a real-time adaptive threshold for data annotation, considering uncertainty, time, budget constraints, and, most importantly, the convergence rate and confidence level constraints. Our extensive simulations on realistic non-independent and identically distributed (non-IID) datasets prove that the DRL-FedUSS framework outperforms baseline approaches, achieving superior intrusion detection accuracy, reducing the associated cost, and accelerating the convergence rate with minimal network traffic labeled data.
Original language | English |
---|---|
Title of host publication | GLOBECOM 2023 - 2023 IEEE Global Communications Conference |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1253-1258 |
Number of pages | 6 |
ISBN (Electronic) | 9798350310900 |
DOIs | |
Publication status | Published - 2023 |
Event | 2023 IEEE Global Communications Conference, GLOBECOM 2023 - Kuala Lumpur, Malaysia Duration: 4 Dec 2023 → 8 Dec 2023 |
Publication series
Name | Proceedings - IEEE Global Communications Conference, GLOBECOM |
---|---|
ISSN (Print) | 2334-0983 |
ISSN (Electronic) | 2576-6813 |
Conference
Conference | 2023 IEEE Global Communications Conference, GLOBECOM 2023 |
---|---|
Country/Territory | Malaysia |
City | Kuala Lumpur |
Period | 4/12/23 → 8/12/23 |
Keywords
- DRL
- Intrusion Detection
- Next Generation Networks
- Semi-supervised Federated learning
- Threat Landscape
Fingerprint
Dive into the research topics of 'DRL-based Federated Uncertainty-guided Semi-Supervised Learning for Network Traffic Selection and Threshold Determination in ZSM'. Together they form a unique fingerprint.Projects
- 1 Active
-
EX-QNRF-AICC-3: Smart, Connected and Autonomous Vehicle and Energy systems for efficient, safe, secure, and sustainable transportation in metropolitan cities
Abdallah, M. M. (Lead Principal Investigator), Al Fuqaha, A. (Principal Investigator), Al-Kuwari, S. M. S. A. (Principal Investigator), Hassaan, M. (Graduate Student), Assistant-1, R. (Research Assistant), Associate-1, R. (Research Associate), MENOUAR, D. H. (Principal Investigator), Abdulhadi, M. Y. (Principal Investigator), Hamood, M. (Graduate Student), Assistant-2, R. (Research Assistant), Karkoub, P. M. (Principal Investigator) & Bouhali, O. (Principal Investigator)
1/02/23 → 1/02/26
Project: Applied Research