Gmail

Summary

Gmail is a free email service provided by Google. it had 1.5 billion active users worldwide making it the largest email service in the world. A user typically accesses Gmail in a web browser or through the official mobile application. Google also supports the use of email clients via the POP and IMAP protocols. At its launch in 2004, Gmail provided a storage capacity of one gigabyte per user, which was significantly higher than its competitors offered at the time. Today, the service comes with 15 gigabytes of storage for free, which is divided among other Google services, such as Google Drive, and Google Photos. Users in need of more storage can purchase Google One to increase this 15GB limit. Users can receive emails up to 50 megabytes in size, including attachments, while they can send emails up to 25 megabytes. In order to send larger files, users can insert files from Google Drive into the message. Gmail has a search-oriented interface and a "conversation vie

Official source

https://en.wikipedia.org/wiki/Gmail

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Artificial Immune System For Collaborative Spam Filtering

Jean-Yves Le Boudec, Slavisa Sarafijanovic

Artificial immune systems (AIS) use the concepts and algorithms inspired by the theory of how the human immune system works. This document presents the design and initial evaluation of a new artificial immune system for collaborative spam filtering. Collaborative spam filtering allows for the detection of not-previously-seen spam content, by exploiting its bulkiness. Our system uses two novel and possibly advantageous techniques for collaborative spam filtering. The first novelty is local processing of the signatures created from the emails prior to deciding whether and which of the generated signatures will be exchanged with other collaborating antispam systems. This processing exploits both the email-content profiles of the users and implicit or explicit feedback from the users, and it uses customized AIS algorithms. The idea is to enable only good quality and effective information to be exchanged among collaborating antispam systems. The second novelty is the representation of the email content, based on a sampling of text strings of a predefined length and at random positions within the emails, and a use of a custom similarity hashing of these strings. Compared to the existing signature generation methods, the proposed sampling and hashing are aimed at achieving a better resistance to spam obfuscation (especially text additions) - which means better detection of spam, and a better precision in learning spam patterns and distinguishing them well from normal text - which means lowering the false detection of good emails. Initial evaluation of the system shows that it achieves promising detection results under modest collaboration, and that it is rather resistant under the tested obfuscation. In order to confirm our understanding of why the system performed well under this initial evaluation, an additional factorial analysis should be done. Also, evaluation under more sophisticated spammer models is necessary for a more complete assessment of the system abilities.

2007

Springer Verlag2008

Spam is usually sent in bulk. A bulk mailing consists of many copies of the same original spam message, each sent to a different recipient. The copies are usually obfuscated, i.e. modified a bit in order to look different from each other. In collaborative spam filtering it is important to determine which emails belong to the same bulk. This allows, after observing an initial portion of a bulk, for the bulkiness scores to be assigned to the remaining emails from the same bulk. This also allows the individual evidence of spamminess to be joined, if such evidence is generated by collaborating filters or users for some of the emails from an initial portion of the bulk. Then, the observed bulkiness and the estimated spamminess of a bulk can be used to better filter the remaining emails from the same bulk. The work by Damiani et al. [2] ("open-digest paper") is well know and often cited for its positive findings about the properties of a digest-based collaborative spam detection technique. The technique produces similar digests out of similar emails, and uses them to find out which emails belong to the same bulk. Based on the experimental evaluation, the paper suggests that the technique provides bulk-spam detection that is robust to increased obfuscation efforts by spammers, and low miss-detection of good emails. We first repeat and extend some of the open-digest paper [2] experiments, using the simplest spammer model from that paper. We find that the conclusions of the open-digest paper are rather miss- leading. Then we propose and evaluate, under the same spammer model, a modified version of the original digest technique. The modified version greatly improves the resistance of spam detection against increased obfuscation effort by spammers, while keeping miss-detection of good emails at a similar level. Based on the observed results, we discuss possible additional modifications and algorithms that could be added on top of the modified digest technique to further improve its filtering performance.

2008

Artificial Immune System For Collaborative Spam Filtering

Jean-Yves Le Boudec, Slavisa Sarafijanovic

2007

Springer Verlag2008

2008