Hussain Sajjad, Aslam Waqar, Mehmood Arif, Choi Gyu Sang, Ashraf Imran
Department of Information Security, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.
Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.
PeerJ Comput Sci. 2024 Mar 26;10:e1834. doi: 10.7717/peerj-cs.1834. eCollection 2024.
Identification of the Internet of Things (IoT) devices has become an essential part of network management to secure the privacy of smart homes and offices. With its wide adoption in the current era, IoT has facilitated the modern age in many ways. However, such proliferation also has associated privacy and data security risks. In the case of smart homes and smart offices, unknown IoT devices increase vulnerabilities and chances of data theft. It is essential to identify the connected devices for secure communication. It is very difficult to maintain the list of rules when the number of connected devices increases and human involvement is necessary to check whether any intruder device has approached the network. Therefore, it is required to automate device identification using machine learning methods. In this article, we propose an accuracy boosting model (ABM) using machine learning models of random forest and extreme gradient boosting. Featuring engineering techniques are employed along with cross-validation to accurately identify IoT devices such as lights, smoke detectors, thermostat, motion sensors, baby monitors, socket, TV, security cameras, and watches. The proposed ensemble model utilizes random forest (RF) and extreme gradient boosting (XGB) as base learners with adaptive boosting. The proposed ensemble model is tested with extensive experiments involving the IoT Device Identification dataset from a public repository. Experimental results indicate a higher accuracy of 91%, precision of 93%, recall of 93%, and F1 score of 93%.
物联网（IoT）设备的识别已成为网络管理的重要组成部分，以保障智能家居和办公室的隐私安全。随着物联网在当今时代的广泛应用，它在许多方面推动了现代社会的发展。然而，这种广泛应用也带来了相关的隐私和数据安全风险。在智能家居和智能办公的场景中，未知的物联网设备会增加漏洞和数据被盗的可能性。为了实现安全通信，识别已连接的设备至关重要。当连接设备数量增加时，维护规则列表变得非常困难，并且需要人工检查是否有任何入侵设备接入网络。因此，需要使用机器学习方法实现设备识别自动化。在本文中，我们提出了一种使用随机森林和极端梯度提升机器学习模型的精度提升模型（ABM）。我们采用了工程技术并结合交叉验证，以准确识别诸如灯光、烟雾探测器、恒温器、运动传感器、婴儿监视器、插座、电视、安全摄像头和手表等物联网设备。所提出的集成模型利用随机森林（RF）和极端梯度提升（XGB）作为基于自适应提升的基础学习器。我们使用来自公共存储库的物联网设备识别数据集进行了广泛的实验，对所提出的集成模型进行了测试。实验结果表明，该模型具有91%的较高准确率、93%的精确率、93%的召回率和93%的F1分数。
