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Intrusion Detection of IoT Traffic Payload Based on Parallel Neural Networks

Published: 24 July 2024 Publication History

Abstract

A malicious traffic intrusion detection method based on packet payload is proposed to address the current flow based intrusion detection models that overly rely on statistical information from packet headers and ignore the characteristics of packet payloads. Firstly, by using the packet payload labeling algorithm, the labeled payload data is obtained, and then the data is cleaned and standardized; Input it into the constructed composite neural network, use parallel neural networks to extract spatial features of payloads, and finally input the extracted features into the fully connected layer for classification. To demonstrate the feasibility of this research method, experimental results on the UNSW-NB15 dataset of the Internet of Things showed that the method is practical and feasible, achieving a higher F1 score compared to the comparative method.

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  1. Intrusion Detection of IoT Traffic Payload Based on Parallel Neural Networks

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    CSAIDE '24: Proceedings of the 2024 3rd International Conference on Cyber Security, Artificial Intelligence and Digital Economy
    March 2024
    676 pages
    ISBN:9798400718212
    DOI:10.1145/3672919
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 24 July 2024

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