使用混合集合深度学习框架的物联网网络入侵检测和识别系统

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Yanika Kongsorot;Pakarat Musikawan;Phet Aimtongkham;Ilsun You;Abderrahim Benslimane;Chakchai So-In
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引用次数: 0

摘要

由于互联网服务的指数级激增和入侵的复杂性,传统的入侵检测算法由于其有限的表示能力和物联网(IoT)相关数据在遥测和网络流量方面的不均衡性而无法处理复杂的入侵。在本研究中,我们从深度学习在特征提取和表示学习方面的成就中汲取灵感,提出了一种精确的混合集合深度学习框架(HEDLF),以防范对物联网网络的模糊网络攻击。为解决复杂特征并缓解不平衡问题,所提出的 HEDLF 包括三个关键组件:1)基于深度学习的分层特征表示技术,旨在通过监督梯度信息的损失来提取特定信息;2)平衡旋转特征提取器,同时鼓励集合分类器的个体准确性和多样性;3)作为聚合方法的元分类器,利用半解析组正则化器来分析基础分类器的输出。此外,这些改进还考虑到了类的不平衡性。实验结果表明,在准确度、精确度、召回率和 F1 分数方面,与最先进的技术相比,所提出的 HEDLF 可以在遥测数据和网络流量数据上取得令人满意的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Intrusion Detection and Identification System for Internet of Things Networks Using a Hybrid Ensemble Deep Learning Framework
Owing to the exponential proliferation of internet services and the sophistication of intrusions, traditional intrusion detection algorithms are unable to handle complex invasions due to their limited representation capabilities and the unbalanced nature of Internet of Things (IoT)-related data in terms of both telemetry and network traffic. Drawing inspiration from deep learning achievements in feature extraction and representation learning, in this study, we propose an accurate hybrid ensemble deep learning framework (HEDLF) to protect against obfuscated cyber-attacks on IoT networks. To address complex features and alleviate the imbalance problem, the proposed HEDLF includes three key components: 1) a hierarchical feature representation technique based on deep learning, which aims to extract specific information by supervising the loss of gradient information; 2) a balanced rotated feature extractor that simultaneously encourages the individual accuracy and diversity of the ensemble classifier; and 3) a meta-classifier acting as an aggregation method, which leverages a semisparse group regularizer to analyze the base classifiers’ outputs. Additionally, these improvements take class imbalance into account. The experimental results show that when compared against state-of-the-art techniques in terms of accuracy, precision, recall, and F1-score, the proposed HEDLF can achieve promising results on both telemetry and network traffic data.
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来源期刊
IEEE Transactions on Sustainable Computing
IEEE Transactions on Sustainable Computing Mathematics-Control and Optimization
CiteScore
7.70
自引率
2.60%
发文量
54
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