基于深度LSTM的智能家居物联网设备网络入侵检测方法

S. Azumah, Nelly Elsayed, Victor Adewopo, Zaghloul Saad Zaghloul, Chengcheng Li
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引用次数: 15

摘要

当今世界物联网(IoT)设备的技术进步已经为许多用户带来了好处,但却存在无人关注的安全问题。物联网设备能够连接到互联网上的其他设备,从任何地方传输和共享数据。因此,需要保护这些设备,因为它们提高了生活质量的舒适度。研究表明,大约70%的物联网设备很容易被黑客入侵。因此,迫切需要一种有效的机制来保护这些设备,特别是在智能家居中。本文提出了一种新的基于深度学习的异常检测方法,用于预测智能家居物联网网络设备的网络攻击,并使用物联网网络入侵数据集学习随着时间的推移发生的新异常值。该模型基于长期记忆架构,与智能家居中现有的最先进的物联网网络异常检测模型相比,该模型的准确性有了显着提高。
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A Deep LSTM based Approach for Intrusion Detection IoT Devices Network in Smart Home
The technological advancement of the Internet of Things (IoT) devices in our world today has become beneficial to many users but has security issues that are left unattended. IoT devices have the ability to connect to other devices on the internet to transmit and share data from anywhere. Hence, the need to secure these devices as they improve the quality of life comfort. Research shows that about 70% of IoT devices are easy to hack. Therefore, an efficient mechanism is highly needed to safeguard these devices, especially in smart homes. This paper proposes a novel deep learning-based anomaly detection approach to predict cyberattacks on smart home IoT network devices and learn new outliers as they occur over time using IoT network intrusion datasets. The proposed model is based on long-term memory architecture, which achieves a significant accuracy improvement compared to the existing state-of-the-art anomaly detection models for IoT networks in smart homes.
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