Email is a critical Internet application, and its security is important. The Sender Policy Framework (SPF), DomainKeys Identified Mail (DKIM), and Domain-based Message Authentication, Reporting, and Conformance (DMARC) were developed to enable mail servers to detect and reject email coming from fraudulent sources. In this paper we study the state of SPF, DKIM, and DMARC validation across a large number of mail servers, the first such study at scale that we know of. We consider two behaviors of sender-validating mail servers: behavior when an email with a valid sender is received and behavior when an email from a invalid sender is received. Our techniques allow us to elicit SPF, DKIM, and DMARC validation behavior of the servers without spam. We find that as many as 85% of mail servers are deploying SPF validation, and over half are deploying all three mechanisms: SPF, DKIM, and DMARC. We also observe there are some nuanced behaviors with regard to adherence to the SPF specification.

Multimedia have significant impact on the social and psychological development of children who are often explored to inappropriate materials, including movies that are either accessible online or through other multimedia channels. Even though not all movies are bad, there are negative effects of offensive languages, violence, and sexuality as exhibited in movies. Parents and guidance of children need all the help they can get to promote the healthy use of movies these days. To offer them appropriate movies of interest to their youths, we have developed MovReC, a personalized movie recommender for children, which is designed to provide educational and suitable entertaining opportunities for children. Unlike Amazon and other online movie recommendation systems, such as Common Sense Media, IMDb, and TasteKid, MovReC is unique, since to the best of our knowledge MovReC is the first personalized children movie recommender. Moreover, MovReC determines the appealingness of a movie for a particular user based on its children-appropriate score computed by using the Backpropagation model, pre-defined category using LDA, its predicted rating using Matrix Factorization, and sentiments based on its users' reviews, which along with its like/dislike count and genres, yield the features considered by MovReC. MovReC combines these features by using the CombMNZ model to rank and recommend movies. The performance evaluation of MovReC clearly demonstrates its effectiveness and its recommended movies are highly regarded by its users.