dc.contributor.advisor | Alam, Md. Ashraful | |
dc.contributor.author | Das, Robat | |
dc.contributor.author | Hossain, Md. Mukhter | |
dc.contributor.author | Islam, Shariful | |
dc.contributor.author | Siddiki, Abujarr | |
dc.date.accessioned | 2019-07-18T06:10:45Z | |
dc.date.available | 2019-07-18T06:10:45Z | |
dc.date.copyright | 2019 | |
dc.date.issued | 2019-05 | |
dc.identifier.other | ID 13101130 | |
dc.identifier.other | ID 14301131 | |
dc.identifier.other | ID 13201005 | |
dc.identifier.other | ID 13321060 | |
dc.identifier.uri | http://hdl.handle.net/10361/12395 | |
dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2019. | en_US |
dc.description | Cataloged from PDF version of thesis. | |
dc.description | Includes bibliographical references (pages 32-33). | |
dc.description.abstract | In recent years, we have seen a huge paradigm shift in business because of the
fast development of the Web. For this reason, consumers change their tendency
from customary shopping to the electronic business. In the time of electronic and
versatile trade, huge quantities of money related exchanges are directed online on
regular schedule, which created opportunities for new potential scheming chances.
By utilizing the unknown structure of theWeb, attackers set out new procedures like
phishing, to fool people with the utilization of false sites to gather their delicate data.
It gather datas such as account IDs, usernames, passwords, credit card information
and so on. In spite of the fact that organizations and software companies uses
methodologies such as heuristics, visual and machine learning to prevent phishing
attacks, still these can't keep the majority of the phishing assaults. In this paper, we
evaluate the model by using LSTM technique by comparing it with previous studies
and we will try to nd out the best features in LSTM. | en_US |
dc.description.statementofresponsibility | Robat Das | |
dc.description.statementofresponsibility | Md. Mukhter Hossain | |
dc.description.statementofresponsibility | Shariful Islam | |
dc.description.statementofresponsibility | Abujarr Siddiki | |
dc.format.extent | 33 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 | Neural network | en_US |
dc.subject | Phishing attack | en_US |
dc.subject | Scheming | en_US |
dc.subject | Machine learning | en_US |
dc.subject | LSTM | en_US |
dc.subject.lcsh | Neural networks (Computer science) | |
dc.subject.lcsh | Machine learning. | |
dc.title | Learning a deep neural network for predicting phishing website | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, Brac University | |
dc.description.degree | B. Computer Science and Engineering | |