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.

翻译：在硬件设备上部署基于机器学习的入侵检测系统（IDS）面临计算资源、功耗和网络连接等方面的限制，因此亟需针对此类约束设计鲁棒且高效的深度学习模型。本文提出一种自动训练与进化量化神经网络（NN）模型的设计方法，该模型大小仅为最先进神经网络的千分之一，却能够以高精度高效分析网络数据进行入侵检测。当部署到FPGA时，该网络使用的查找表（LUT）数量相比先前工作减少了2.3至8.5倍，同时性能保持相当。