dc.contributor.advisor | Mostakim, Moin | |
dc.contributor.author | Zaman, Syed Mahbubuz | |
dc.contributor.author | Haque, A. B. M. Abrar | |
dc.contributor.author | Nayeem, Mehedi Hassan | |
dc.contributor.author | Sagor, Misbah Uddin | |
dc.date.accessioned | 2021-10-19T05:41:28Z | |
dc.date.available | 2021-10-19T05:41:28Z | |
dc.date.copyright | 2021 | |
dc.date.issued | 2021-01 | |
dc.identifier.uri | http://hdl.handle.net/10361/15427 | |
dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. | en_US |
dc.description | Cataloged from PDF version of thesis. | |
dc.description | Includes bibliographical references (pages 35-37). | |
dc.description.abstract | Nowadays e-mail is being used by millions of people as an effective form of formal or informal communication over the Internet and with this high-speed form of communication there comes a more effective form of threat known as spam. Spam e-mail is often called junk e-mails which are unsolicited and sent in bulk. By these unsolicited emails, the Internet users are hugely impacted in terms of security concerns as well as being exposed to contents that are not appropriate for certain users. There is no way to stop spammers using static filters because almost every other day they find a new way to bypass the filter. New techniques are introduced to elude this system. In this paper, a smart and dynamic(adaptive) system is proposed that will be using Random Weight Network (RWN) to approach spam in a different way and meanwhile this will also detect the most relevant features that will help to design the spam filter. A spam filter with the capability of identifying spam automatically will also be embedded in the proposed system. Also a comparison of different parameters for different RWN models have been shown to determine which model works best with what parameters under different situations. | en_US |
dc.description.statementofresponsibility | Syed Mahbubuz Zaman | |
dc.description.statementofresponsibility | A. B. M. Abrar Haque | |
dc.description.statementofresponsibility | Mehedi Hassan Nayeem | |
dc.description.statementofresponsibility | Misbah Uddin Sagor | |
dc.format.extent | 37 pages | |
dc.language.iso | en | en_US |
dc.publisher | Brac University | en_US |
dc.rights | Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. | |
dc.subject | Spam filtering | en_US |
dc.subject | Email spam detection | en_US |
dc.subject | Feature analysis | en_US |
dc.subject | Long Short Term Memory | en_US |
dc.subject.lcsh | Spam (Electronic mail) | |
dc.subject.other | ID 16201017 | |
dc.subject.other | ID 17101078 | |
dc.subject.other | ID 17101261 | |
dc.title | Dynamic spam detection system and most relevant features identification using random weight network | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, Brac University | |
dc.description.degree | B. Computer Science | |