Network intrusion detection using neural networks on FPGA SoCs

Lenos Ioannou; Suhaib A. Fahmy

doi:10.1109/FPL.2019.00043

Network intrusion detection using neural networks on FPGA SoCs

Lenos Ioannou, Suhaib A. Fahmy

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

18 Scopus citations

Abstract

Network security is increasing in importance as systems become more interconnected. Much research has been conducted on large appliances for network security, but these do not scale well to lightweight systems such as those used in the Internet of Things (IoT). Meanwhile, the low power processors used in IoT devices do not have the required performance for detailed packet analysis. We present an approach for network intrusion detection using neural networks, implemented on FPGA SoC devices that can achieve the required performance on embedded systems. The design is flexible, allowing model updates in order to adapt to emerging attacks.

Original language	English (US)
Title of host publication	Proceedings - 29th International Conference on Field-Programmable Logic and Applications, FPL 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	232-238
Number of pages	7
ISBN (Print)	9781728148847
DOIs	https://doi.org/10.1109/FPL.2019.00043
State	Published - Sep 1 2019
Externally published	Yes

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10.1109/FPL.2019.00043

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title = "Network intrusion detection using neural networks on FPGA SoCs",

abstract = "Network security is increasing in importance as systems become more interconnected. Much research has been conducted on large appliances for network security, but these do not scale well to lightweight systems such as those used in the Internet of Things (IoT). Meanwhile, the low power processors used in IoT devices do not have the required performance for detailed packet analysis. We present an approach for network intrusion detection using neural networks, implemented on FPGA SoC devices that can achieve the required performance on embedded systems. The design is flexible, allowing model updates in order to adapt to emerging attacks.",

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N2 - Network security is increasing in importance as systems become more interconnected. Much research has been conducted on large appliances for network security, but these do not scale well to lightweight systems such as those used in the Internet of Things (IoT). Meanwhile, the low power processors used in IoT devices do not have the required performance for detailed packet analysis. We present an approach for network intrusion detection using neural networks, implemented on FPGA SoC devices that can achieve the required performance on embedded systems. The design is flexible, allowing model updates in order to adapt to emerging attacks.

AB - Network security is increasing in importance as systems become more interconnected. Much research has been conducted on large appliances for network security, but these do not scale well to lightweight systems such as those used in the Internet of Things (IoT). Meanwhile, the low power processors used in IoT devices do not have the required performance for detailed packet analysis. We present an approach for network intrusion detection using neural networks, implemented on FPGA SoC devices that can achieve the required performance on embedded systems. The design is flexible, allowing model updates in order to adapt to emerging attacks.

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BT - Proceedings - 29th International Conference on Field-Programmable Logic and Applications, FPL 2019

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Network intrusion detection using neural networks on FPGA SoCs

Abstract

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