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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2311.04194 |
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| _version_ | 1866911787598741504 |
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| author | Acharya, Rabin Yu Jeune, Laurens Le Mentens, Nele Ganji, Fatemeh Forte, Domenic |
| author_facet | Acharya, Rabin Yu Jeune, Laurens Le Mentens, Nele Ganji, Fatemeh Forte, Domenic |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_04194 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Quantization-aware Neural Architectural Search for Intrusion Detection Acharya, Rabin Yu Jeune, Laurens Le Mentens, Nele Ganji, Fatemeh Forte, Domenic Cryptography and Security 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. |
| title | Quantization-aware Neural Architectural Search for Intrusion Detection |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2311.04194 |