The emerging Internet-of-Things (IoT) are vulnerable to Sybil attacks where attackers can manipulate fake identities or abuse pseudoidentities to compromise the effectiveness of the IoT and even disseminate spam. In this paper, we survey Sybil attacks and defense schemes in IoT. Specifically, we first define three types Sybil attacks: SA-1, SA-2, and SA-3 according to the Sybil attacker’s capabilities. We then present some Sybil defense schemes, including social graph-based Sybil detection (SGSD), behavior classification-based Sybil detection (BCSD), and mobile Sybil detection with the comprehensive comparisons. Finally, we discuss the challenging research issues and future directions for

Abstract—In this paper, we propose a Social-based Mobile Sybil Detection (SMSD) scheme to detect Sybil attackers from their abnormal contacts and pseudonym changing behaviors. Specifically, we first define four levels of Sybil attackers in mobile environments according to their attacking capabilities. We then exploit mobile users ’ contacts and their pseudonym changing behaviors to distinguish Sybil attackers from normal users. To alleviate the storage and computation burden of mobile users, the cloud server is introduced to store mobile user’s contact information and to perform the Sybil detection. Furthermore, we utilize a ring structure associated with mobile user’s contact signatures to resist the contact forgery by mobile users and cloud servers. In addition, investigating mobile user’s contact distribution and social proximity, we propose a semi-supervised learning with Hidden Markov Model to detect the colluded mobile users. Security analysis demonstrates that the SMSD can resist the Sybil attackers from the defined four levels, and the extensive trace-driven simulation shows that the SMSD can detect these Sybil attackers with high accuracy. I.

Multimedia is progressively becoming content-driven and object-oriented, promoting applica-tions with user collaboration in the current fashion. In 2012, in every minute of the day,

Mobile sensing has become a promising paradigm for mobile users to obtain information by task crowdsourcing. However, due to the social preferences of mobile users, the quality of sensing reports may be impacted by the underlying social attributes and selfishness of individuals. Therefore, it is crucial to consider the social impacts and trustworthiness of mobile users when selecting task participants in mobile sensing. In this paper, we propose a Social Aware Crowdsourcing with Reputation Management (SACRM) scheme to select the well-suited participants and allocate the task rewards in mobile sensing. Specifically, we consider the social attributes, task delay and reputation in crowdsourcing and propose a participant selection scheme to choose the well-suited participants for the sensing task under a fixed task budget. A report assessment and rewarding scheme is also introduced to measure the quality of the sensing reports and allocate the task rewards based the assessed report quality. In addition, we develop a reputation management scheme to evaluate the trustworthiness and cost performance ratio of mobile users for participant selection. Theoretical analysis and extensive simulations demonstrate that SACRM can efficiently improve the crowdsourcing utility and effectively stimulate the participants to improve the quality of their sensing reports.