A hybrid DNN–LSTM model for detecting phishing URLs

dc.authorid0000-0003-3345-8344en_US
dc.contributor.authorÖzcan, Alper
dc.contributor.authorÇatal, Çağatay
dc.contributor.authorDönmez, Emrah
dc.contributor.authorŞentürk, Behçet
dc.date.accessioned2021-08-20T20:10:45Z
dc.date.available2021-08-20T20:10:45Z
dc.date.issued2021en_US
dc.departmentMTÖ Üniversitesi, Sosyal ve Beşeri Bilimler Fakültesi, Yönetim Bilişim Sistemleri Bölümüen_US
dc.description.abstractPhishing is an attack targeting to imitate the official websites of corporations such as banks, e-commerce, financial institutions, and governmental institutions. Phishing websites aim to access and retrieve users’ important information such as personal identification, social security number, password, e-mail, credit card, and other account information. Several anti-phishing techniques have been developed to cope with the increasing number of phishing attacks so far. Machine learning and particularly, deep learning algorithms are nowadays the most crucial techniques used to detect and prevent phishing attacks because of their strong learning abilities on massive datasets and their state-of-the-art results in many classification problems. Previously, two types of feature extraction techniques [i.e., character embedding-based and manual natural language processing (NLP) feature extraction] were used in isolation. However, researchers did not consolidate these features and therefore, the performance was not remarkable. Unlike previous works, our study presented an approach that utilizes both feature extraction techniques. We discussed how to combine these feature extraction techniques to fully utilize from the available data. This paper proposes hybrid deep learning models based on long short-term memory and deep neural network algorithms for detecting phishing uniform resource locator and evaluates the performance of the models on phishing datasets. The proposed hybrid deep learning models utilize both character embedding and NLP features, thereby simultaneously exploiting deep connections between characters and revealing NLP-based high-level connections. Experimental results showed that the proposed models achieve superior performance than the other phishing detection models in terms of accuracy metric.en_US
dc.identifier.citationOzcan, A., Catal, C., Donmez, E., & Senturk, B. (2021). A hybrid DNN–LSTM model for detecting phishing URLs. Neural Computing and Applications, 1-17.en_US
dc.identifier.doi10.1007/s00521-021-06401-z
dc.identifier.endpage17en_US
dc.identifier.issn0941-0643en_US
dc.identifier.issn1433-3058en_US
dc.identifier.scopus2-s2.0-85112070505en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-021-06401-z
dc.identifier.urihttps://hdl.handle.net/20.500.12899/336
dc.identifier.wosWOS:000682813600001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorDönmez, Emrah
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofNeural Computing and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectPhishingen_US
dc.subjectMachine learningen_US
dc.subjectDeep learningen_US
dc.subjectPhishing detectionen_US
dc.titleA hybrid DNN–LSTM model for detecting phishing URLsen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Yükleniyor...
Küçük Resim
İsim:
Alper Özcan - Makale Dosyası.pdf
Boyut:
1017.81 KB
Biçim:
Adobe Portable Document Format
Açıklama:
Full Text / Tam Metin
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama: