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.

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Cloud radio access network is one of the most promising cellular networks for the next generation of mobile networks. The basic idea of cloud RAN (radio access network) is virtualizing and centralizing the intelligent part of the base station, the base band unit, and keeping remote radio heads on cell site enabling a centralized processing and management. Offloading data computation to edge cloud was proposed as a solution to deal with resource limitation while keeping a good quality of service. In this paper, we propose a strategy to jointly handle offloading decision and offloading request scheduling in cloud RAN. We aim to improve network quality of service while reducing the scheduling cost expressed in terms of overload, network delay, and migration cost. Numerical results show that the proposed approach is able to reduce the response time of the applications, mobile terminal energy consumption, and total execution cost.

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