| Title | Machine Learning-Based Threat Identification of Industrial Internet |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Cai, Junhui, Li, Qianmu |
| Conference Name | 2020 IEEE International Conference on Progress in Informatics and Computing (PIC) |
| Keywords | Heuristic algorithms, industrial control, Industrial Internet, Kernel, machine learning, machine learning algorithms, Measurement, privacy, pubcrawl, Real-time Systems, security, telecommunication traffic, threat identification, threat vectors |
| Abstract | In order to improve production and management efficiency, traditional industrial control systems are gradually connected to the Internet, and more likely to use advanced modern information technologies, such as cloud computing, big data technology, and artificial intelligence. Industrial control system is widely used in national key infrastructure. Meanwhile, a variety of attack threats and risks follow, and once the industrial control network suffers maliciously attack, the loss caused is immeasurable. In order to improve the security and stability of the industrial Internet, this paper studies the industrial control network traffic threat identification technology based on machine learning methods, including GK-SVDD, RNN and KPCA reconstruction error algorithm, and proposes a heuristic method for selecting Gaussian kernel width parameter in GK-SVDD to accelerate real-time threat detection in industrial control environments. Experiments were conducted on two public industrial control network traffic datasets. Compared with the existing methods, these methods can obtain faster detection efficiency and better threat identification performance. |
| DOI | 10.1109/PIC50277.2020.9350752 |
| Citation Key | cai_machine_2020 |
Machine Learning-Based Threat Identification of Industrial Internet