###### Abstract  
In this paper, a layered architecture incorporating Blockchain technology (BCT) and Machine Learning (ML) is proposed in the context of the Industrial Internet-of-Things (IIoT) for smart manufacturing applications. The proposed architecture is composed of five layers covering sensing, network/protocol, transport enforced with BCT components, application and advanced services (i.e., BCT data, ML and cloud) layers. BCT enables gathering sensor access control information, while ML brings its effectivity in attack detection such as DoS (Denial of Service), DDoS (Distributed Denial of Service), injection, man in the middle (MitM), brute force, cross-site scripting (XSS) and scanning attacks by employing classifiers differentiating between normal and malicious activity. The design of our architecture is compared to similar ones in the literature to point out potential benefits. Experiments, based on the IIoT dataset, have been conducted to evaluate our contribution, using four metrics: Accuracy, Precision, Sensitivity and Matthews Correlation Coefficient (MCC). Artificial Neural Network (ANN), Decision Tree (DT), Random Forest, Naive Bayes, AdaBoost and Support Vector Machine (SVM) classifiers are evaluated regarding these four metrics. Even if more experiments are required, it is illustrated that the proposed architecture can reduce significantly the number of DDoS, injection, brute force and XSS attacks and threats within an advanced framework for sensor access control in IIoT networks based on a smart contract along with ML classifiers. Blockchain; industrial IoT; smart manufacturing; security threats; security solutions; machine learning; classifiers; privacy; smart contract; access control +
Footnote †: journal: _Sci_  
system is one example of a system based on the IIoT including sensors and actuators controlled by programmable logic controllers (PLCs) [4]. However, the integration of these new digital technologies will lead to an increased number of vulnerabilities and attacks in the smart manufacturing context [5]. In that context, Machine Learning (ML) and Blockchain technology (BCT) can be viewed as a potential solution to reduce the new vulnerability introduced in IIoT fields for smart manufacturing. On the one hand, ML, which is a subfield of artificial intelligence dealing with the development of algorithms, can improve automatically through experience and by the use of data [6]. There already exists ML-based security solutions for IIoT [4,7,8]. ML is used in smart manufacturing and IIoT to improve decisions and has therefore become one key element of smart manufacturing [9]. On the other hand, the use of BCT for the smart manufacturing, which is a technology based on recording transactions among participants implemented via blocks inside databases, can be fruitful, leading to several benefits such as automation process, traceability, data integrity and sustainability. One challenge is the integration of IoT-related technology by considering the huge diversity of communication protocols, IoT device variety and the big data exchanged among various IoT applications. Moreover, there is no common model to support the IoT hardware and software diversity in the context of smart manufacturing [10]. Likewise, ML algorithms can be integrated in models based on BCT for data analysis and prediction to enforce security and for attacks detection as well. The aim of this work is thus to propose a secure IIoT architecture including BCT enforced with ML algorithms for smart manufacturing applications. The proposed architecture is based on a previous work [11] developed in the specific context of IoT networks. Our architecture is compared to similar ones in the literature [12, 13, 14] in terms of number of layers, advantages, disadvantages and security issues. Through experimentations, it is demonstrated that our architecture can reduce the number of attacks and can mitigate threats thanks to the BCT inherent features for sensors' access control. Our experiments have been realized in two steps.