利用物联网数据驱动的 BOA-CNN-BiGRU-AAM 网络分类加强网络异常入侵检测

Suresh G, Sathya M, Arthi D, Arulkumaran G
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引用次数: 0

摘要

网络安全是网络安全异常入侵检测的关键组成部分之一,它负责识别网络中可能表明可能存在安全漏洞或威胁的异常行为或活动。在这个建议的入侵检测系统(IDS)中,网络流量数据通过异常检测进行持续监控。这项研究利用了最新的数据集之一,即 IoTID20 数据集,来发现连接到物联网的网络中的异常行为,从而促进这一过程。预处理阶段包括平滑、过滤和清理数据等艰苦步骤。本研究在特征选择过程中引入了松果优化算法(PCOA),这是一种受大自然启发的新型优化算法。PCOA 试图提高特征选择的有效性,同时从松树的各种繁殖方式中汲取灵感,如授粉以及松果在动物和重力作用下的移动。此外,IDS 采用基于卷积神经网络的双向门控递归单元-附加注意机制(CNN-BiGRU-AAM)进行分类,利用深度学习的能力高效地完成分类任务。此外,这项工作还提出了用于超参数调整的肉毒杆菌优化算法(BOA),该算法以肉毒杆菌在人体解剖学中的作用方式为模型。BOA 使用一种基于人的方法来调整模型的超参数,以达到最佳准确度。实验结果表明,建议的方法能有效改进网络异常入侵检测系统,最高准确率可达 99.45%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing Network Anomaly Intrusion Detection with IoT Data-Driven BOA-CNN-BiGRU-AAM -Net Classification
Network security is one of the key components of cybersecurity anomaly intrusion detection, which is responsible for identifying unusual behaviours or activities within a network that might indicate possible security breaches or threats. In this suggested intrusion detection system (IDS), network traffic data is continuously monitored via anomaly detection. The study makes utilising one of the most recent datasets to spot unusual behaviour in networks connected to the Internet of Things, the IoTID20 dataset, to facilitate this process. The preprocessing stage involves painstaking steps for smoothing, filtering, and cleaning the data. The Pine Cone Optimisation algorithm (PCOA), a novel optimizer inspired by nature, is introduced in this study for the feature selection process. PCOA seeks to increase the effectiveness of feature selection while drawing inspiration from the various ways that pine trees reproduce, such as pollination and the movement of pine cones by animals and gravity. Moreover, IDS is classified using Bidirectional Gated Recurrent Unit–Additive Attention Mechanism Based on Convolutional Neural Networks (CNN-BiGRU-AAM), which makes use of deep learning's capabilities for efficient classification tasks. In addition, this work presents the Botox Optimisation Algorithm (BOA) for hyperparameter tuning, which is modelled after the way Botox functions in human anatomy. BOA uses a human-based method to adjust the hyperparameters of the model to attain the best accuracy. The results of the experiments show that the suggested methodologies are effective in improving network anomaly intrusion detection systems, with a maximum accuracy of 99.45%.
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