Towards fast and lightweight spam account detection in mobile social networks through fog computing - Zhang, Jiahao; Li, Qiang; Wang, Xiaoqi; Feng, Bo; Guo, Dong (Peer-to-peer networking and applications, 2018)
Now, mobile devices play an increasingly important role in social networks by sharing information quickly, such as mobile phones and wearable health surveillance devices. Mobile social networks are vulnerable to spammers because of the fragile security policies of mobile operating systems. Especially, social networks on mobile devices face many difficulties in defending against spammers due to their low computing power, poor network quality and long response time. Since graph-based algorithms require huge computing power, machine-learning classifiers require very short response time, and existing PC-based research is not suitable for mobile devices, we need a lightweight and fast response method for mobile devices to detect spammers in mobile social networks. Regarded as the extension of cloud computing, fog computing puts the data, data processing and applications in the devices that are at the edge of the Internet (without storing all of them in the cloud), which leads to a better real-time performance, adapts to the wide geographical distribution and the high mobility of mobile devices. In this paper, we propose COLOR + , a method based on fog computing that performs most computations at terminal (mobile devices). It only uses the interaction between the account and its neighbors, which makes it easy to store and calculate a local graph on a mobile device. Each interaction value can be applied to any request. COLOR + detects spammers based on a threshold of the suspicion degree. We collect 50 million normal accounts and about 40,000 spammers from Twitter. Experiments show that the accuracy of COLOR + is about 85.95%, whose average time to detect an account is 0.01s. Therefore, COLOR + is an effective detection method that can be quickly applied.