Establishing software-defined network for fraud detection on energy fraud and traffic classification test case(s)

Abstract

The increase of fraudsters and fraud attacks on the communication network plays a major role in the loss of revenue, network abuse, and degradation of services. Communication and cloud networks belong to separate companies and consist of handover points from one network to another. The scope is to bring the communication and cloud networks closer and understand network traffic profiles using Software-Defined Networking (SDN) concept. The SDN controller will serve to route the extracted tapped data to a central server instead of making use of the live network to route traffic. To apply policies in an automated method and identify which user’s traffic can be a fraud case, and further to send a block signal to the network charging element on the communication network. The traffic flow process will be redesigned using the SDN controller within the architecture for a seamless approach and maintaining the performance of the network. This article explores energy fraud identification and traffic classification based on four test cases to define a new framework for communication and cloud providers to enable the detection and blocking of fraud. The aim of this study is to perform real-time data classification; reporting of fraudulent subscribers, applications, and protocols; real-time analytics; and real-time blocking of data is classified as a fraudulent activity within the mobile data network. The main purpose is to achieve a higher energy/service fraud detection rate than similar work in the field, to provide a solution that integrates within the traditional network, and further blocks the fraud within the network.