DOI码：10.3390/su15032204
所属单位：哈尔滨工程大学船舶工程学院
发表刊物：Sustainability
刊物所在地：瑞士
关键字：deep learning models; IoT security; optimization algorithms
摘要：Because of the rise in the number of cyberattacks, the devices that make up the Internet of Things (IoT) environment are experiencing increased levels of security risks. In recent years, a significant number of centralized systems have been developed to identify intrusions into the IoT environment. However, due to diverse requirements of IoT devices such as dispersion, scalability, resource restrictions, and decreased latency, these strategies were unable to achieve notable outcomes. The present paper introduces two novel metaheuristic optimization algorithms for optimizing the weights of deep learning (DL) models, use of DL may help in the detection and prevention of cyberattacks of this nature. Furthermore, two hybrid DL classifiers, i.e., convolutional neural network (CNN) + deep belief network (DBN) and bidirectional long short-term memory (Bi-LSTM) + gated recurrent network (GRU), were designed and tuned using the already proposed optimization algorithms, which results in ads to improved model accuracy. The results are evaluated against the recent approaches in the relevant field along with the hybrid DL classifier. Model performance metrics such as accuracy, rand index, f-measure, and MCC are used to draw conclusions about the model’s validity by employing two distinct datasets. Reg
合写作者：Nasib Singh Gill,Pradeep Kumar Singh
第一作者：Amit Sagu
论文类型：期刊论文
通讯作者：洪维强
论文编号：2204
学科门类：工学
文献类型：J
卷号：15
期号：3
ISSN号：2071-1050
是否译文：否
发表时间：2023-01-01
收录刊物：SCI
发布期刊链接：https://www.mdpi.com/2071-1050/15/3/2204