| Title | Research on Classification of Intrusion Detection in Internet of Things Network Layer Based on Machine Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Liu, Jingyu, Yang, Dongsheng, Lian, Mengjia, Li, Mingshi |
| Conference Name | 2021 IEEE International Conference on Intelligence and Safety for Robotics (ISR) |
| Date Published | mar |
| Keywords | Classification algorithms, composability, Data models, feature extraction, Intrusion detection, network architecture, Predictive Metrics, pubcrawl, Resiliency, Sensors, Support vector machines |
| Abstract | The emergence of the Internet of Things (IoT) is not only a global revolution in the information industry, but also brought tremendous changes to our lives. With the development of the technology and means of the IoT, information security issues have gradually emerged, and intrusion attacks have become one of the main problems of the IoT network security. The network layer of the IoT is the key connecting the platform and sensors or controllers of the IoT, and it is also the most standardized, the strongest and the most mature part of the whole physical network architecture. Its large-scale development has led to the network layer's security issues will receive more attention and face more challenges. This paper proposes an intrusion detection algorithm deployed on the network layer of the IoT, which uses the BPSO algorithm to extract features from the NSL-KDD dataset, and applies support vector machines (SVM) as the core model of the algorithm to detect and identify abnormal data, especially DoS attacks. Experimental results show that the model's detection rate of abnormal data and DoS attacks are significantly improved. |
| DOI | 10.1109/ISR50024.2021.9419529 |
| Citation Key | liu_research_2021 |
Research on Classification of Intrusion Detection in Internet of Things Network Layer Based on Machine Learning