Dynamic spam detection system and most relevant features identification using random weight network

Dynamic spam detection system and most relevant features identification using random weight network

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.

Bibliographic Details
Main Authors: Zaman, Syed Mahbubuz, Haque, A. B. M. Abrar, Nayeem, Mehedi Hassan, Sagor, Misbah Uddin
Other Authors: Mostakim, Moin
Format: Thesis
Language:English
Published: Brac University 2021
Subjects:
Spam filtering
Email spam detection
Feature analysis
Long Short Term Memory
Spam (Electronic mail)
ID 16201017
ID 17101078
ID 17101261
Online Access:http://hdl.handle.net/10361/15427
id 10361-15427
record_format dspace
spelling 10361-154272022-01-26T10:15:47Z Dynamic spam detection system and most relevant features identification using random weight network Zaman, Syed Mahbubuz Haque, A. B. M. Abrar Nayeem, Mehedi Hassan Sagor, Misbah Uddin Mostakim, Moin Department of Computer Science and Engineering, Brac University Spam filtering Email spam detection Feature analysis Long Short Term Memory Spam (Electronic mail) ID 16201017 ID 17101078 ID 17101261 This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 35-37). 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. Syed Mahbubuz Zaman A. B. M. Abrar Haque Mehedi Hassan Nayeem Misbah Uddin Sagor B. Computer Science 2021-10-19T05:41:28Z 2021-10-19T05:41:28Z 2021 2021-01 Thesis http://hdl.handle.net/10361/15427 en 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. 37 pages application/pdf Brac University
institution Brac University
collection Institutional Repository
language English
topic Spam filtering
Email spam detection
Feature analysis
Long Short Term Memory
Spam (Electronic mail)
ID 16201017
ID 17101078
ID 17101261
spellingShingle Spam filtering
Email spam detection
Feature analysis
Long Short Term Memory
Spam (Electronic mail)
ID 16201017
ID 17101078
ID 17101261
Zaman, Syed Mahbubuz
Haque, A. B. M. Abrar
Nayeem, Mehedi Hassan
Sagor, Misbah Uddin
Dynamic spam detection system and most relevant features identification using random weight network
description This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
author2 Mostakim, Moin
author_facet Mostakim, Moin
Zaman, Syed Mahbubuz
Haque, A. B. M. Abrar
Nayeem, Mehedi Hassan
Sagor, Misbah Uddin
format Thesis
author Zaman, Syed Mahbubuz
Haque, A. B. M. Abrar
Nayeem, Mehedi Hassan
Sagor, Misbah Uddin
author_sort Zaman, Syed Mahbubuz
title Dynamic spam detection system and most relevant features identification using random weight network
title_short Dynamic spam detection system and most relevant features identification using random weight network
title_full Dynamic spam detection system and most relevant features identification using random weight network
title_fullStr Dynamic spam detection system and most relevant features identification using random weight network
title_full_unstemmed Dynamic spam detection system and most relevant features identification using random weight network
title_sort dynamic spam detection system and most relevant features identification using random weight network
publisher Brac University
publishDate 2021
url http://hdl.handle.net/10361/15427
work_keys_str_mv AT zamansyedmahbubuz dynamicspamdetectionsystemandmostrelevantfeaturesidentificationusingrandomweightnetwork
AT haqueabmabrar dynamicspamdetectionsystemandmostrelevantfeaturesidentificationusingrandomweightnetwork
AT nayeemmehedihassan dynamicspamdetectionsystemandmostrelevantfeaturesidentificationusingrandomweightnetwork
AT sagormisbahuddin dynamicspamdetectionsystemandmostrelevantfeaturesidentificationusingrandomweightnetwork
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