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Network Anomaly Detection Using Federated Learning

Conference · · MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM)
 [1];  [1];  [1]
  1. University of Texas at El Paso,Department of Computer Science,El Paso,USA
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Due to the veracity and heterogeneity in network traffic, detecting anomalous events is challenging. The computational load on global servers is a significant challenge in terms of efficiency, accuracy, and scalability. Our primary motivation is to introduce a robust and scalable framework that enables efficient network anomaly detection. We address the issue of scalability and efficiency for network anomaly detection by leveraging federated learning, in which multiple participants train a global model jointly. Unlike centralized training architectures, federated learning does not require participants to upload their training data to the server, preventing attackers from exploiting the training data. Moreover, most prior works have focused on traditional centralized machine learning, making federated machine learning under-explored in network anomaly detection. Therefore, we propose a deep neural network framework that could work on low to mid-end devices detecting network anomalies while checking if a request from a specific IP address is malicious or not. Compared to multiple traditional centralized machine learning models, the deep neural federated model reduces training time overhead. The proposed method performs better than baseline machine learning techniques on the UNSW-NB15 data set as measured by experiments conducted with an accuracy of 97.21% and a faster computation time.

Research Organization:
Univ. of Texas at El Paso, TX (United States)
Sponsoring Organization:
USDOE Office of Fossil Energy (FE), Clean Coal and Carbon Management
Contributing Organization:
The University of Texas at El Paso
DOE Contract Number:
FE0032089
OSTI ID:
1959004
Journal Information:
MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM), Conference: MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM); Related Information: The anomaly detection dataset was used from the below public source -https://research.unsw.edu.au/projects/unsw-nb15-dataset
Country of Publication:
United States
Language:
English

References (11)

UNADA: Unsupervised Network Anomaly Detection Using Sub-space Outliers Ranking book January 2011
Federated Deep Learning for Collaborative Intrusion Detection in Heterogeneous Networks conference September 2021
Client-Edge-Cloud Hierarchical Federated Learning conference June 2020
Communication-efficient hierarchical federated learning for IoT heterogeneous systems with imbalanced data journal March 2022
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection journal January 2021
Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset journal November 2020
Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning conference November 2020
Machine Learning in Network Anomaly Detection: A Survey journal January 2021
Condition monitoring and anomaly detection in cyber-physical systems conference June 2022
UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set) conference November 2015
Network anomaly detection research: a survey journal March 2019

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