# Blockchain Secured Dynamic Machine Learning Pipeline for Manufacturing
## 1 Introduction  
Artificial intelligence (AI) and machine learning (ML) have emerged as key technologies in information security due to their ability to rapidly analyse millions of events and identify a wide range of threats. Blockchain is distinguished by its advantages of decentralised data storage and program execution, as well as the immutability of the data. Blockchain and AI can work together to provide secure data storage and data sharing, and to analyse the blockchain audit trail to more accurately understand relationships of data changes. Combining the two technologies will give blockchain-based business networks a new level of intelligence by allowing them to read, understand, and connect data at lightning speed [1]. ML has been established as the most promising way to learn patterns from data. Its applications can be found in the data processing of web browsing, financial data, health care, autonomous automobiles, and almost every other data-driven industry around us [2]. Because of the vast range of applications, ML models are now being run on a wide range of devices, from low-end IoT and mobile devices to high-performance clouds and data centres, to offer both training and inference services [3]. Especially in industrial production, machine downtimes are costly for companies and can have a significant impact on the entire company [4]. Therefore, AI applications used in the business world must be fail-safe, secure, and reliable. Trust in AI applications that are used in industrial production can only be built if they meet the requirements for information security [5]. Next to the typical cybersecurity threats, the ML-based threats must be added to these requirements, which are threats against ML data, ML models, the entire ML pipelines, etc. [6]. To build trust, all actors must make sure they are following the rules, and their actions must be verifiable and believable to their partners. For accountability, the goals of security protection are for the assets to be available, safe, private, and handled in a way that obeys the law (e.g., privacy). Machine learning applications are, in fact, pipelines that link several parts and rely on large amounts of data for training and testing [7]. Data are also required for maintaining and upgrading machine learning models, since they take user data as input and use it to come up with insights. The importance of data in machine learning cannot be overstated, as data flows across the whole machine learning process. Widespread attacks threat ML security and privacy, these range from ML model stealing [8], \"model inversion\" [9], \"model poisoning\" [10], \"data poisoning\" [11], \"data inference\" [12] to \"membership/attribute inference\" [13] as well as other attacks. In ML, security and privacy problems are caused by complex pipelines that use multiple system and software stacks to offer current features such as acceleration. A full ML pipeline includes collecting raw data, training, inference, prediction, and possibly retraining and reusing the ML model. The pipeline may be segmented since data owners, ML computation hosts, model owners, and output recipients are likely separate businesses [14]. As a result, ML models frequently have weak resilience, as shown in adversarial cases or poisoning assaults. A small change in the way training is conducted could have huge negative effects that are hard to spot. In comparison, blockchain has an entirely different purpose and characteristics. Blockchain is a technology to store data, immutable and decentralised. The data are distributed across a large network of nodes so that it is available even if some nodes fail. Once a block has been added by agreement among participants, it cannot be deleted or changed, even by the original authors. The data are publicly available but not publicly readable without a digital key [15]. One obvious use is to save records of success and credit, such as any entity credentials and trust amounts related to it [16].