| Title | Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Authors | Zolanvari, Maede, Teixeira, Marcio A., Gupta, Lav, Khan, Khaled M., Jain, Raj |
| Journal | IEEE Internet of Things Journal |
| Volume | 6 |
| Pagination | 6822—6834 |
| Date Published | aug |
| ISSN | 2327-4662 |
| Keywords | Big Data analytics, command injection attacks, composability, cyber attack, cyber-attacks, cyber-vulnerability assessment, Data analysis, electromagnetic interference, IDS, IIoT systems, Industrial Internet of Things, Industrial Internet of Things (IIoT), Internet of Things, Intrusion detection, intrusion detection solutions, intrusion detection system, learning (artificial intelligence), machine learning, machine learning (ML), Metrics, Network security, production engineering computing, Protocols, pubcrawl, resilience, Resiliency, security of data, SQL, Structured Query Language, structuredquery language injection attacks, supervisory control and data acquisition (SCADA), vulnerability assessment |
| Abstract | It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning (ML) and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of ML in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using ML models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a ML-based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods. |
| DOI | 10.1109/JIOT.2019.2912022 |
| Citation Key | zolanvari_machine_2019 |
Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things