Quantization-aware Neural Architectural Search for Intrusion Detection

• URL: http://arxiv.org/abs/2311.04194v2
• Date: Sat, 2 Mar 2024 04:20:10 GMT
• Title: Quantization-aware Neural Architectural Search for Intrusion Detection
• Authors: Rabin Yu Acharya, Laurens Le Jeune, Nele Mentens, Fatemeh Ganji, Domenic Forte,
• Abstract summary: We present a design methodology that automatically trains and evolves quantized neural network (NN) models that are a thousand times smaller than state-of-the-art NNs. The number of LUTs utilized by this network when deployed to an FPGA is between 2.3x and 8.5x smaller with performance comparable to prior work.
• Score: 5.010685611319813
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deploying machine learning-based intrusion detection systems (IDSs) on hardware devices is challenging due to their limited computational resources, power consumption, and network connectivity. Hence, there is a significant need for robust, deep learning models specifically designed with such constraints in mind. In this paper, we present a design methodology that automatically trains and evolves quantized neural network (NN) models that are a thousand times smaller than state-of-the-art NNs but can efficiently analyze network data for intrusion at high accuracy. In this regard, the number of LUTs utilized by this network when deployed to an FPGA is between 2.3x and 8.5x smaller with performance comparable to prior work.

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