Electronic transactions with cryptocurrency systems based on blockchain in our days have become very popular due to the good reputation of this technology. However, that good reputation cannot deny the serious anomalies and the risks that can cause these cryptocurrencies. In this work, we propose a new model for anomaly detection over bitcoin electronic transactions. We used in our proposal two machine learning algorithms, namely the One Class Support Vector Machines (OCSVM) algorithm to detect outliers and the K-Means algorithm in order to group the similar outliers with the same type of anomalies. We evaluated our work by generating detection results and we obtained high performance results on accuracy.

With the increasing number of connected devices and the number of online transactions today, managing all these transactions and devices and maintaining network security is a research issue. Current solutions are mainly based on cloud computing infrastructures, which require servers high-end and broadband networks to provide data storage and computing services. These solutions have a number of significant disadvantages, such as high maintenance costs of centralized servers, critical weakness of Internet Of Things applications, security and trust issues, etc. The blockchain is seen as a promising technique for addressing the mentioned security issues and design new decentralization frameworks. However, this new technology has a great potential in the most diverse technological fields. In this paper, we focus on presenting an overview of blockchain technology, highlighting its advantages, limitations and areas of application. The originality of this work resides in the comparison between the different blockchain systems and their security schemes and the perspective of integrating this technology into secured systems models for our comfort and our private life.