DLSAS: Distributed Large-Scale Anti-Spam Framework for Decentralized Online Social Networks

Abstract

In the last decade, researchers and the open source community have proposed various Decentralized Online Social Networks (DOSNs) that remove dependency on centralized online social network providers to preserve user privacy. However, transitioning from centralized to decentralized environment creates various new set of problems, such as adversarial manipulations. In this paper, we present DLSAS, a novel unsupervised and decentralized anti-spam framework for DOSNs. DLSAS provides decentralized spam detection that is resilient to adversarial attacks. DLSAS typifies massively parallel frameworks and exploits fully decentralized learning and cooperative approaches. Furthermore, DLSAS provides a novel defense mechanism for DOSNs to prevent malicious nodes participating in the system by creating a validation overlay to asses the credibility of the exchanged information among the participating nodes and exclude the misbehaving nodes from the system. Extensive experiments using Twitter datasets confirm not only the DLSAS's capability to detect spam with higher accuracy compared to state-of-the-art approaches, but also the DLSAS's robustness against different adversarial attacks.