Intrusion Detection in IoT Using Deep Residual Networks with Attention Mechanisms

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Bo Cui, Yachao Chai, Zhen Yang, Keqin Li
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引用次数: 0

Abstract

Connected devices in IoT systems usually have low computing and storage capacity and lack uniform standards and protocols, making them easy targets for cyberattacks. Implementing security measures like cryptographic authentication, access control, and firewalls for IoT devices is insufficient to fully address the inherent vulnerabilities and potential cyberattacks within the IoT environment. To improve the defensive capabilities of IoT systems, some research has focused on using deep learning techniques to provide new solutions for intrusion detection systems. However, some existing deep learning-based intrusion detection methods suffer from inadequate feature extraction and insufficient model generalization capability. To address the shortcomings of existing detection methods, we propose an intrusion detection model based on temporal convolutional residual modules. An attention mechanism is introduced to assess feature scores and enhance the model’s ability to concentrate on critical features, thereby boosting its detection performance. We conducted extensive experiments on the ToN_IoT dataset and the UNSW-NB15 dataset, and the proposed model achieves accuracies of 99.55% and 89.23% on the ToN_IoT and UNSW-NB15 datasets, respectively, with improvements of 0.14% and 15.3% compared with the current state-of-the-art models. These results demonstrate the superior detection performance of the proposed model.
利用带有注意机制的深度残差网络进行物联网入侵检测
物联网系统中的连接设备通常计算和存储能力较低,缺乏统一的标准和协议,因此很容易成为网络攻击的目标。为物联网设备实施密码验证、访问控制和防火墙等安全措施不足以完全解决物联网环境中固有的漏洞和潜在的网络攻击。为了提高物联网系统的防御能力,一些研究侧重于利用深度学习技术为入侵检测系统提供新的解决方案。然而,现有的一些基于深度学习的入侵检测方法存在特征提取不充分、模型泛化能力不足等问题。针对现有检测方法的不足,我们提出了一种基于时序卷积残差模块的入侵检测模型。我们引入了一种注意力机制来评估特征得分,增强模型集中于关键特征的能力,从而提高其检测性能。我们在 ToN_IoT 数据集和 UNSW-NB15 数据集上进行了大量实验,结果表明所提出的模型在 ToN_IoT 数据集和 UNSW-NB15 数据集上的准确率分别达到了 99.55% 和 89.23%,与目前最先进的模型相比分别提高了 0.14% 和 15.3%。这些结果证明了所提出模型的卓越检测性能。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
期刊介绍: ACS Applied Bio Materials is an interdisciplinary journal publishing original research covering all aspects of biomaterials and biointerfaces including and beyond the traditional biosensing, biomedical and therapeutic applications. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important bio applications. The journal is specifically interested in work that addresses the relationship between structure and function and assesses the stability and degradation of materials under relevant environmental and biological conditions.
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