LAUSR

The use of smart contracts enhances the capabilities of blockchain-based botnets, allowing for greater information capacity, richer application scenarios, and the deployment of program functions directly on the blockchain. However, smart blockchains offer a better solution for the intelligence of IoT systems, but they also come with some security risks. Botnet is a highly insecure community because it is used to do hazardous things like Distributed Denial of Service (DDoS). It is extremely essential to detect botnets with some useful tools, such as artificial intelligence (AI) algorithms, because these algorithms can assist us to monitor the network automatically. We need to pay the utmost attention to some feature engineering work, as recognition rates of AI models are considerably improved with suitable features. In this article, we propose domain embedding (DE) models to generate low-dimensional features for domains with unsupervised learning algorithms. We also explore some key parameters of the DE model to obtain decent effects on domain features. A modified version of the $k$ -means algorithm called extended $k$ -means, is used to cluster these domains in certain hubs and botnets that can be found for smart blockchain-based IoT systems. In the experiments, some domain correlation scores can be computed during the DE model, and similar domains have higher correlation scores.