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Network Anomaly Detection in Distributed Edge Computing Infrastructure

Conference ·
 [1];  [1];  [1];  [1]
  1. University of Texas at El Paso,Department of Computer Science,El Paso,USA
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As networks continue to grow in complexity and scale, detecting anomalies has become increasingly challenging, particularly in diverse and geographically dispersed environments. Traditional approaches often struggle with managing the computational burden associated with analyzing large-scale network traffic to identify anomalies. This paper introduces a distributed edge computing framework that integrates federated learning with Apache Spark and Kubernetes to address these challenges. We hypothesize that our approach, which enables collaborative model training across distributed nodes, significantly enhances the detection accuracy of network anomalies across different network types. We show that by leveraging distributed computing and containerization technologies, our framework not only improves scalability and fault tolerance but also achieves superior detection performance compared to state-of-the-art methods. Extensive experiments on the UNSW-NB15 and ROAD datasets validate the effectiveness of our approach, demonstrating statistically significant improvements in detection accuracy and training efficiency over baseline models, as confirmed by MannWhitney U and Kolmogorov-Smirnov tests (p<0.05).

Research Organization:
National Energy Technology Laboratory
Sponsoring Organization:
US Department of Energy
DOE Contract Number:
FE0032089
OSTI ID:
2583899
Country of Publication:
United States
Language:
English

References (9)

Kolmogorov–Smirnov Test: Overview other September 2014
Deep-Learning Based Detection for Cyber-Attacks in IoT Networks: A Distributed Attack Detection Framework journal February 2023
Clustered federated learning architecture for network anomaly detection in large scale heterogeneous IoT networks journal August 2023
Distributed attack detection scheme using deep learning approach for Internet of Things journal May 2018
Collaborative Anomaly Detection for Internet of Things based on Federated Learning conference August 2020
Network Anomaly Detection Using Federated Learning conference November 2022
UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set) conference November 2015
Checkpointing Strategies to Tolerate Non-Memoryless Failures on HPC Platforms journal March 2024
A comprehensive guide to CAN IDS data and introduction of the ROAD dataset journal January 2024

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