A Systematic Literature Review on Cyber Security Threats of Industrial Internet of Things
Ravi Gedam, Surendra Rahamatkar · 2022
In recent years, the Industrial Internet of Things (IIoT) has become one of the popular technology among Internet users for transportation, business, education, and communication development. With the rapid adoption of IoT technology, individuals and organizations easily communicate with each other without great effort from the remote location. Although, IoT technology often confronts unauthorized access to sensitive data, personal safety risks, and different types of attacks. Hence, it is essential to model the IoT technology with proper security measures to cope up with the rapid increase of IoT-enabled devices in the real-time market. In particular, predicting security threats is significant in the Industrial IoT applications due to the huge impact on production, financial loss, or injuries. Also, the heterogeneity of the IoT environment necessitates the inherent analysis to detect or prevent the attacks over the voluminous IoT-generated data. Even though the IoT network employs machine learning and deep learning-based security mechanisms, the resource constraints create a set-back in the security provisioning especially, in maintaining the trade-off between the IoT devices’ capability and the security level. Hence, in-depth analysis of the IoT data along with the time efficiency is crucial to proactively predict the cyber-threats. Despite this, relearning the new environment from the scratch leads to the time-consuming process in the large-scale IoT environment when there are minor changes in the learning environment while applying the static machine learning or deep learning models. To cope up with this constraint, incrementally updating the learning environment is essential after learning the partially changed environment with the knowledge of previously learned data. Hence, to provide security to the resource-constrained IoT environment, selecting the potential input data for the incremental learning model and fine-tuning the parameters of the deep learning model for the input data is vital, which assists towards the proactive prediction of the security threats by the time-efficient learning of the dynamically arriving input data.