Visible to the public Oases: An Online Scalable Spam Detection System for Social Networks

TitleOases: An Online Scalable Spam Detection System for Social Networks
Publication TypeConference Paper
Year of Publication2018
AuthorsXu, H., Hu, L., Liu, P., Xiao, Y., Wang, W., Dayal, J., Wang, Q., Tang, Y.
Conference Name2018 IEEE 11th International Conference on Cloud Computing (CLOUD)
Date Publishedjul
Keywordscommunity-based opportunities, content polluters, decentralized DHT-based tree overlay deployment, DHT based overlay, Distributed databases, Human Behavior, Internet, invasive software, load-balancing, malware disseminators, Metrics, Oases, online scalable spam detection system, online social networks, pattern classification, Peer-to-peer computing, progressive aggregation tree, pubcrawl, resource allocation, Routing, Scalability, social communities, social networking (online), social spam, spam classifiers, spam detection, spammers, Training, trees (mathematics), Twitter, Twitter data, unsolicited e-mail, Vegetation, Web-based social networks
AbstractWeb-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.
Citation Keyxu_oases:_2018