| dc.contributor.advisor | Fu, Xiaoming Prof. Dr. | de |
| dc.contributor.author | Hameed, Sufian | de |
| dc.date.accessioned | 2012-09-26T15:51:31Z | de |
| dc.date.accessioned | 2013-01-18T13:23:22Z | de |
| dc.date.available | 2013-01-30T23:50:59Z | de |
| dc.date.issued | 2012-09-26 | de |
| dc.identifier.uri | http://hdl.handle.net/11858/00-1735-0000-000D-F070-F | de |
| dc.identifier.uri | http://dx.doi.org/10.53846/goediss-2544 | |
| dc.identifier.uri | http://dx.doi.org/10.53846/goediss-2544 | |
| dc.format.mimetype | application/pdf | de |
| dc.language.iso | eng | de |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/ | de |
| dc.title | Leveraging Email based Social Networks to Prevent Spam: Framework, System Design and Evaluation | de |
| dc.type | doctoralThesis | de |
| dc.title.translated | Leveraging Email based Social Networks to Prevent Spam: Framework, System Design and Evaluation | de |
| dc.contributor.referee | Fu, Xiaoming Prof. Dr. | de |
| dc.date.examination | 2012-09-06 | de |
| dc.subject.dnb | 004 Informatik | de |
| dc.subject.gok | EGIM 100 | de |
| dc.subject.gok | EGIM 120 | de |
| dc.description.abstracteng | Spam is still an open problem from the
network operator's perspective. The common state-of-the-art
strategy is to place filters against spam at the recipient's edge.
Although this strategy largely solves the spam problem from the
user's perspective but the false positives/negatives may still
exist. This strategy is also unable to prevent spam from traversing
the Internet. Consequently, we now have around 260 billion spam
messages sent across the Internet each day. Spam is therefore
consuming large amounts of Internet bandwidth and is imposing
non-negligible financial loss to network operators. Therefore it
becomes imperative to mitigate spam much earlier than is typically
done today. The main part of this thesis proposes LENS, a novel,
easily adaptable and scalable spam protection system that is
incrementally deployable with low processing overheads. LENS
leverages the recipient's social network to allow correspondence
within the social network to directly pass to the mailbox of the
recipient. LENS further mitigates spam beyond social circles and
stops spam messages early on, instead of filtering these messages
from user mailboxes or at the recipient's edge. The key idea in
LENS is to select legitimate and authentic users, called
Gatekeepers (GKs), from outside the recipient's social circle and
within predefined social distances. LENS utilizes the GK to
generate a voucher, and new senders are required to obtain these
vouchers (to communicate with the recipient) from a GK in their
social neighborhood. Recipients recover from compromised GKs simply
by selecting replacements and revoking vouchers. Unless a GK
vouches for the emails of potential senders from outside the social
circle of a particular recipient, those e-mails are prevented from
transmission. In this way LENS drastically reduces the consumption
of Internet bandwidth by spam. The contributions of this thesis are
the development of social network based spam mitigation framework
(LENS), a system design based on this framework, the evaluation of
the system by means of simulation and a prototype implementation.
The evaluations show that with the help of hundreds of GKs, LENS
can provide reliable email delivery from millions of potential
users. LENS imposes Zero overhead for the common case of frequent
and familiar senders, and remains lightweight for the general case.
Using real email traces, the simulations show that LENS is
effective in accepting all legitimate inbound emails. Our prototype
implementation of LENS in Postfix/MailAvenger shows that LENS
consumes up to 75% less CPU, 9% less memory and it is around 2-3
orders of magnitude faster in processing emails than traditional
solutions like SpamAssassin. | de |
| dc.contributor.coReferee | Hogrefe, Dieter Prof. Dr. | de |
| dc.subject.topic | Mathematics and Computer Science | de |
| dc.subject.ger | Social Network | de |
| dc.subject.ger | Email Communication | de |
| dc.subject.ger | Social Trust | de |
| dc.subject.ger | Email Social Network | de |
| dc.subject.ger | Spam Prevention | de |
| dc.subject.eng | Social Network | de |
| dc.subject.eng | Email Communication | de |
| dc.subject.eng | Social Trust | de |
| dc.subject.eng | Email Social Network | de |
| dc.subject.eng | Spam Prevention | de |
| dc.subject.bk | 54.32 | de |
| dc.subject.bk | 54.38 | de |
| dc.identifier.urn | urn:nbn:de:gbv:7-webdoc-3702-8 | de |
| dc.identifier.purl | webdoc-3702 | de |
| dc.affiliation.institute | Mathematisch-Naturwissenschaftliche Fakultäten | de |
| dc.identifier.ppn | 737898879 | de |