Email spam consumes a lot of network resources and threatens many systems because of its unwanted or malicious content. Most existing spam filters only target complete-spam but ignore semispam. This paper proposes a novel and comprehensive CPSFS scheme: Credible Personalized Spam Filtering Scheme, which classifies spam into two categories: complete-spam and semispam, and targets filtering both kinds of spam. Complete-spam is always spam for all users; semispam is an email identified as spam by some users and as regular email by other users. Most existing spam filters target complete-spam but ignore semispam. In CPSFS, Bayesian filtering is deployed at email servers to identify complete-spam, while semispam is identified at client side by crowdsourcing. An email user client can distinguish junk from legitimate emails according to spam reports from credible contacts with the similar interests. Social trust and interest similarity between users and their contacts are calculated so that spam reports are more accurately targeted to similar users. The experimental results show that the proposed CPSFS can improve the accuracy rate of distinguishing spam from legitimate emails compared with that of Bayesian filter alone.

SUMMARYThe micro-blog network is one of the most popular social networking platforms. By calculating the degree centrality, we found that hub nodes play an important role in micro-blog networks, which is the main power of message forwarding. To improve the security of the micro-blog network, we proposed a defending scheme against malicious Uniform Resource Locator (URL) diffusing in micro-blog networks with hub nodes. After a node found a new malicious URL, it will edit a warning massage about the malicious URL. If the normal node obtains malicious URL warning message, it will send private message with the warning message to the hub node and update its blog article. The malicious URL warning messages spread rapidly in the whole networks in a short period because of the influence of Hub nodes. At the same time, we add the comparison mechanism to reduce the redundancy of spreading the warning message in the networks. So the security of entire micro-blog networks can be improved against malicious URL without increasing the network load. Experiments show that our scheme can effectively defend against the malicious URL in the any scale of micro-blog networks.