A Novel Intrusion Detection Method for Internet of Things
| Title | A Novel Intrusion Detection Method for Internet of Things |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Li, Peisong, Zhang, Ying |
| Conference Name | 2019 Chinese Control And Decision Conference (CCDC) |
| Date Published | jun |
| Keywords | Artificial neural networks, belief networks, Biological cells, composability, DBN, deep belief network, Deep Learning, embedded system, genetic algorithms, Internet of Things, Internet of Things era, intrusion attacks, Intrusion detection, intrusion detection method, intrusion detection model, Intrusion Detection Systems, IoT networks, IoT system, IoT technology, Metrics, network intrusion detection, Neurons, optimal network structure, pubcrawl, Resiliency, security of data |
| Abstract | Internet of Things (IoT) era has gradually entered our life, with the rapid development of communication and embedded system, IoT technology has been widely used in many fields. Therefore, to maintain the security of the IoT system is becoming a priority of the successful deployment of IoT networks. This paper presents an intrusion detection model based on improved Deep Belief Network (DBN). Through multiple iterations of the genetic algorithm (GA), the optimal network structure is generated adaptively, so that the intrusion detection model based on DBN achieves a high detection rate. Finally, the KDDCUP data set was used to simulate and evaluate the model. Experimental results show that the improved intrusion detection model can effectively improve the detection rate of intrusion attacks. |
| DOI | 10.1109/CCDC.2019.8832753 |
| Citation Key | li_novel_2019 |
- intrusion attacks
- security of data
- optimal network structure
- Neurons
- network intrusion detection
- Metrics
- IoT technology
- IoT system
- IoT networks
- Intrusion Detection Systems
- intrusion detection model
- intrusion detection method
- Intrusion Detection
- belief networks
- Internet of Things era
- Internet of Things
- genetic algorithms
- Embedded System
- deep learning
- Deep Belief Network
- DBN
- Biological cells
- Artificial Neural Networks
- Resiliency
- composability
- pubcrawl