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GPU-based Classification for Wireless Intrusion Detection

Authors:

Kesheng WuAuthors Info & Claims

SNTA '21: Proceedings of the 2021 on Systems and Network Telemetry and Analytics

Pages 27 - 31

https://doi.org/10.1145/3452411.3464445

Published: 15 June 2021 Publication History

Abstract

Automated network intrusion detection systems (NIDS) continuously monitor the network traffic to detect attacks or/and anomalies. These systems need to be able to detect attacks and alert network engineers in real-time. Therefore, modern NIDS are built using complex machine learning algorithms that require large training datasets and are time-consuming to train. The proposed work shows that machine learning algorithms from the RAPIDS cuML library on Graphics Processing Units (GPUs) can speed-up the training process on large scale datasets. This approach is able to reduce the training time while providing high accuracy and performance. We demonstrate the proposed approach on a large subset of data extracted from the Aegean Wi-Fi Intrusion Dataset (AWID). Multiple classification experiments were performed on both CPU and GPU. We achieve up to 65x acceleration of training several machine learning methods by moving most of the pipeline computations to the GPU and leveraging the new cuML library as well as the GPU version of the CatBoost library.

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Cited By

Sundharajan KA. SMeenaloshini RRoomi S. MAninthitha G(2024)Optimization of Circular Loop Antenna With Step Change in Loop Width Using Machine Learning TechniquesDesign and Optimization of Wearable, Implantable, and Edible Antennas10.4018/979-8-3693-2659-6.ch006(102-126)Online publication date: 31-May-2024
https://doi.org/10.4018/979-8-3693-2659-6.ch006
Mondal BFaisal FTusnia Towshi ZFahad Monir MAhmed T(2023)A Gradient Boosted ML Approach to Feature Selection for Wireless Intrusion Detection2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)10.1109/VTC2023-Spring57618.2023.10199490(1-5)Online publication date: Jun-2023
https://doi.org/10.1109/VTC2023-Spring57618.2023.10199490
Arden FSafitri C(2022)Hyperparameter Tuning Algorithm Comparison with Machine Learning Algorithms2022 6th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)10.1109/ICITISEE57756.2022.10057630(183-188)Online publication date: 13-Dec-2022
https://doi.org/10.1109/ICITISEE57756.2022.10057630

Index Terms

GPU-based Classification for Wireless Intrusion Detection

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Information

Published In

cover image ACM Conferences

SNTA '21: Proceedings of the 2021 on Systems and Network Telemetry and Analytics

June 2021

46 pages

ISBN:9781450383868

DOI:10.1145/3452411

General Chairs:
Massimo Cafaro
Universita del Salento, Italy
,
Jinoh Kim
Texas A&M University, Commerce, USA
,
Alex Sim
Lawrence Berkeley National Laboratory, USA

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

University of Arizona: University of Arizona
SIGHPC: ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing
SIGARCH: ACM Special Interest Group on Computer Architecture

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 June 2021

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Funding Sources

U.S. Department of Energy

Conference

HPDC '21

Sponsor:

University of Arizona
SIGHPC
SIGARCH

HPDC '21: The 30th International Symposium on High-Performance Parallel and Distributed Computing

June 21, 2021

Virtual Event, Sweden

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Overall Acceptance Rate 22 of 106 submissions, 21%

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Citations

Cited By

Sundharajan KA. SMeenaloshini RRoomi S. MAninthitha G(2024)Optimization of Circular Loop Antenna With Step Change in Loop Width Using Machine Learning TechniquesDesign and Optimization of Wearable, Implantable, and Edible Antennas10.4018/979-8-3693-2659-6.ch006(102-126)Online publication date: 31-May-2024
https://doi.org/10.4018/979-8-3693-2659-6.ch006
Mondal BFaisal FTusnia Towshi ZFahad Monir MAhmed T(2023)A Gradient Boosted ML Approach to Feature Selection for Wireless Intrusion Detection2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)10.1109/VTC2023-Spring57618.2023.10199490(1-5)Online publication date: Jun-2023
https://doi.org/10.1109/VTC2023-Spring57618.2023.10199490
Arden FSafitri C(2022)Hyperparameter Tuning Algorithm Comparison with Machine Learning Algorithms2022 6th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)10.1109/ICITISEE57756.2022.10057630(183-188)Online publication date: 13-Dec-2022
https://doi.org/10.1109/ICITISEE57756.2022.10057630
Chatzoglou EKambourakis GKolias CSmiliotopoulos C(2022)Pick Quality Over Quantity: Expert Feature Selection and Data Preprocessing for 802.11 Intrusion Detection SystemsIEEE Access10.1109/ACCESS.2022.318359710(64761-64784)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3183597

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