Quantization-aware Neural Architectural Search for Intrusion Detection

2023/11/07 by Rabin Yu Acharya, Acharya, Rabin Yu, Laurens Le Jeune +7 · 1 citation
Computer Science · #Advanced Malware Detection Techniques #Anomaly Detection Techniques and Applications #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Network Security and Intrusion Detection

paper · pdf · doi:10.48550/arxiv.2311.04194

openalex publication_date 2023/11/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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• A Quantized VAE-MLP Botnet Detection Model: A Systematic Evaluation of Quantization-Aware Training and Post-Training Quantization Strategies

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