Newswise – A new study published in Engineering Presents a new framework that combines automatic learning (ML) and Blockchain technology (BT) to improve IT security in engineering. The frame, named Machine Learning on Blockchain (MLOB), aims to approach the limits of existing ML-BT integration solutions which are mainly focused on data security while neglecting IT security.
The ML has been widely used in engineering to solve complex problems, offering great precision and efficiency. However, it faces security threats such as data falsification and logical corruption. BT, with its characteristics of decentralization, transparency and immutability, has been explored to protect engineering data. But the traditional ML process remains vulnerable to the risk outside chain because the ML models are often executed outside the blockchain.
The MLOB framework places both the data and the calculation process on the blockchain, performing it in the form of intelligent contracts and protecting execution records. It consists of four main components: the acquisition of ML, where an ML model is formed for a specific task; ML conversion, which adapts the model formed for the deployment of the blockchain; ML safety load, ensuring data security and model transfer; and the execution of the ML model based on consensus, guaranteeing the safety and accuracy of the calculations.
To illustrate the effectiveness of the MLOB frame, the researchers developed a prototype and applied it to a task of monitoring the progress of interior construction. They compared the MLOB frame with three baselines and two recent ML-BT integrated approaches. The results have shown that the MLOB frame has considerably improved security, successfully defending itself against six attack scenarios designed. It maintained a high level of precision, with only a difference of 0.001 in the average intersection compared to the metric of the Union (Miou) compared to the best basic method. Although it had a slightly compromised efficiency, with an increase in latency of 0.231 second compared to the most effective basic line, overall performance met the requirements of industrial practice.
The MLOB frame also has managerial implications. He encourages organizations to innovate by integrating advanced technologies, which can lead to more competitive engineering operations. It also helps to mitigate the risks associated with data and logical security, optimizing resource allocation and improving economic resilience.
However, the frame has certain limits. It has a limited support for latency sensitive scenarios and does not have a friendly interface. Future research will focus on optimizing its efficiency and designing a more accessible user interface to further improve its conviviality and expand its application in engineering engineering.
The document “Machine Learning on Blockchain (MLOB): a new paradigm for computer security in engineering”, written by Zhiming Dong, Weisheng Lu. Full text of free access paper: for more information on the EngineeringFollow us on x (https://twitter.com/engineeringjrn) and love us on Facebook (
