Biblio

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.

Recently, the armed forces want to bring the Internet of Things technology to improve the effectiveness of military operations in battlefield. So the Internet of Battlefield Things (IoBT) has entered our view. And due to the high processing latency and low reliability of the “combat cloud” network for IoBT in the battlefield environment, in this paper , a novel “combat cloud-fog” network architecture for IoBT is proposed. The novel architecture adds a fog computing layer which consists of edge network equipment close to the users in the “combat-cloud” network to reduce latency and enhance reliability. Meanwhile, since the computing capability of the fog equipment are weak, it is necessary to implement distributed computing in the “combat cloud-fog” architecture. Therefore, the distributed computing load balancing problem of the fog computing layer is researched. Moreover, a distributed generalized diffusion strategy is proposed to decrease latency and enhance the stability and survivability of the “combat cloud-fog” network system. The simulation result indicates that the load balancing strategy based on generalized diffusion algorithm could decrease the task response latency and support the efficient processing of battlefield information effectively, which is suitable for the “combat cloud- fog” network architecture.