Governance framework for voice spam detection and interception of telecom network

With the rapid development of 5G technology, the number of telecom network users is exploding, which brings great pressure to the governance of the whole network. Voice spam, such as fraud, harassment and other illegal voice calls, not only occupy communication resources, but also disturb people's daily life and social order. In light of this, considering different scenario elements, this paper proposes a governance framework that considers time, location and user attributes simultaneously to identify and intercept voice spam. Specifically, we firstly use decision tree algorithm to recognize and classify voice spam in telecom network. Then, based on the frequent itemset theory, the malicious phone IDs are further analyzed and revealed. The telephone numbers are divided into white list, grey list, personal blacklist and group blacklist. Through simulation analysis and practical application in Zhengzhou area, the framework proposed can accurately detect voice spam and timely remind users of potential risks, providing users with secure voice services.