Paper Info
Title
Oases: An Online Scalable Spam Detection System for Social Networks
Abstract
Web-based social networks enable new community-based opportunities for participants to engage, share their thoughts, and interact with each other. Theses related activities such as searching and advertising are threatened by spammers, content polluters, and malware disseminators. We propose a scalable spam detection system, termed Oases, for uncovering social spam in social networks using an online and scalable approach. The novelty of our design lies in two key components: (1) a decentralized DHT-based tree overlay deployment for harvesting and uncovering deceptive spam from social communities; and (2) a progressive aggregation tree for aggregating the properties of these spam posts for creating new spam classifiers to actively filter out new spam. We design and implement the prototype of Oases and discuss the design considerations of the proposed approach. Our large-scale experiments using real-world Twitter data demonstrate scalability, attractive load-balancing, and graceful efficiency in online spam detection for social networks.
Year
DOI
Venue
2018
10.1109/CLOUD.2018.00020
2018 IEEE 11th International Conference on Cloud Computing (CLOUD)
Keywords
Field
DocType
online social networks,spam detection,DHT based overlay
Social spam,World Wide Web,Software deployment,Social network,Computer science,Peer to peer computing,Distributed database,Novelty,Malware,Scalability,Distributed computing
Conference
ISBN
Citations 
PageRank 
978-1-5386-7236-5
0
0.34
References 
Authors
1
8
Name
Order
Citations
PageRank
Hailu Xu101.69
Liting Hu226316.93
Pinchao Liu302.03
Yao Xiao4137.62
Wentao Wang521.08
Jai Dayal600.68
Qingyang Wang734834.07
Yuzhe Tang814721.06