Adaptive Pruning of Deep Neural Networks for Resource-Aware Embedded Intrusion Detection on the Edge

Alexandre Broggi; Nathaniel Bastian; Lance Fiondella; Gokhan Kul

doi:10.48550/arxiv.2505.14592

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Adaptive Pruning of Deep Neural Networks for Resource-Aware Embedded Intrusion Detection on the Edge

Alexandre Broggi, Nathaniel Bastian, Lance Fiondella and Gokhan Kul

05/20/2025

DOI: https://doi.org/10.48550/arxiv.2505.14592

Abstract

Computer Science - Cryptography and Security

Computer Science - Learning

Artificial neural network pruning is a method in which artificial neural network sizes can be reduced while attempting to preserve the predicting capabilities of the network. This is done to make the model smaller or faster during inference time. In this work we analyze the ability of a selection of artificial neural network pruning methods to generalize to a new cybersecurity dataset utilizing a simpler network type than was designed for. We analyze each method using a variety of pruning degrees to best understand how each algorithm responds to the new environment. This has allowed us to determine the most well fit pruning method of those we searched for the task. Unexpectedly, we have found that many of them do not generalize to the problem well, leaving only a few algorithms working to an acceptable degree.

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Title: Adaptive Pruning of Deep Neural Networks for Resource-Aware Embedded Intrusion Detection on the Edge
Creators: Alexandre Broggi
Nathaniel Bastian
Lance Fiondella
Gokhan Kul
Academic Unit: Department of Computer and Information Science; Department of Electrical and Computer Engineering; Cybersecurity Center
Language: English
Resource Type: Preprint
DOI: https://doi.org/10.48550/arxiv.2505.14592
Record Identifier: 9914443131401301