An Edge Computing Based Situation Enabled Crowdsourcing Blacklisting System for Efficient Identification of Scammer Phone Numbers

The growth of telecommunication fraud has caused tremendous loss to end users. In particular, new technologies such as robocalling systems have been a new resource of harassment. Traditional approaches in detecting such activities simply rely on the construction of blacklisting number systems. However, criminals can easily masquerade their phone numbers simply by changing their numbers through VoIP (Voice over IP) or use virtual mobile numbers (VMN) with relatively low pricing, laxed ID checks and high-level API automation. In this paper, we present a novel situation-enabled approach to blacklist unwanted phone numbers while keeping high detection rate through distributed crowd sourcing. The system consists of two parts. First, we collect a user’s daily schedule in time series as situational data and use the data to train Long Short Term Memory (LSTM) deep learning model. This model is used to predict the user’s situation in the future. Then, we implement a semi-automatic tagging application to tag each incoming call by reading the call history against the predicted situation. An incoming phone number can be automatically tagged as malicious if it is in a wrong situation or could be benign otherwise. A user is also allowed to manually change the tagging afterwards if it is necessary. Second, a distributed crowdsourcing is used to aggregate highly ranked calling numbers from different devices in the same area. When a higher-level blacklist has been built, it can be used to update local ones by propagating back to end user devices with edge local blacklist and edge foreign blacklist. A simple evaluation has been made against real incoming calls on Android phones. The results show that our system design can attain decent detection rates.