Affiliation:
1. Cebu Technological University, Philippines
Abstract
Security for IoT gadgets is an undertaking that has been made more troublesome by the far-reaching utilization of network safety in different applications, including wise modern frameworks, homes, individual devices, and vehicles. The fact that has been introduced makes deep learning for interruption recognition one productive security method. I thought about a few relevant systematic reviews that had already been written. Recent systematic reviews may include older and more recent works on the subject. For better IoT security, late exploration has focused on improving deep learning calculations. The ideal methodology for carrying out interruption recognition in the Internet of Things is determined by looking at the exhibition of different deep learning executions and investigating interruption location techniques that utilise them. Convolutional neural networks (CNNs), long short-term memory (LSTM), and gated recurrent units (GRUs) are the deep learning models used in this review. A standard dataset for IoT interruption identification is considered to evaluate the proposed model. The practical information is then investigated and diverged from current IoT interruption discovery strategies. In contrast with currently utilized approaches, the recommended strategy seems to have the best precision.
Publisher
Mesopotamian Academic Press
Subject
Pathology and Forensic Medicine,Drug Discovery,Pharmaceutical Science,Molecular Medicine,Pharmacology,Molecular Medicine,Genetics,Molecular Biology,Ecology, Evolution, Behavior and Systematics,Physical and Theoretical Chemistry,Condensed Matter Physics,Molecular Biology,Biophysics,Plant Science,Molecular Biology,Plant Science,Soil Science,Agronomy and Crop Science,Molecular Biology,Agronomy and Crop Science,General Medicine,Physiology,Cellular and Molecular Neuroscience,Psychiatry and Mental health,Molecular Biology,Cell Biology,Developmental Biology,Genetics
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献