skip to main content
Visitante
Meu Espaço
Minha Conta
Sair
Identificação
This feature requires javascript
Tags
Revistas Eletrônicas (eJournals)
Livros Eletrônicos (eBooks)
Bases de Dados
Bibliotecas USP
Ajuda
Ajuda
Idioma:
Inglês
Espanhol
Português
This feature required javascript
This feature requires javascript
Primo Search
Busca Geral
Busca Geral
Acervo Físico
Acervo Físico
Produção Intelectual da USP
Produção USP
Search For:
Clear Search Box
Search in:
Busca Geral
Or select another collection:
Search in:
Busca Geral
Busca Avançada
Busca por Índices
This feature requires javascript
This feature requires javascript
A URL-Based Social Semantic Attacks Detection With Character-Aware Language Model
Almousa, May ; Anwar, Mohd
Access, IEEE, 2023, Vol.11, p.10654-10663
IEEE
Sem texto completo
Citações
Citado por
Serviços
Detalhes
Resenhas & Tags
Nº de Citações
This feature requires javascript
Enviar para
Adicionar ao Meu Espaço
Remover do Meu Espaço
E-mail (máximo 30 registros por vez)
Imprimir
Link permanente
Referência
EasyBib
EndNote
RefWorks
del.icio.us
Exportar RIS
Exportar BibTeX
This feature requires javascript
Título:
A URL-Based Social Semantic Attacks Detection With Character-Aware Language Model
Autor:
Almousa, May
;
Anwar, Mohd
Assuntos:
Characterbert
;
convolutional neural network (CNN)
;
Deep learning
;
Feature extraction
;
long short-term memory (LSTM)
;
Phishing
;
Semantics
;
social
engineering
attacks detection
;
Transformers
;
uniform resource locator (URL)
;
Uniform resource locators
;
Unsolicited e-mail
É parte de:
Access, IEEE, 2023, Vol.11, p.10654-10663
Descrição:
Social engineering attacks rely on human errors and behavioral choices. The semantic attack, a subcategory of social engineering attacks, utilizes behavioral or cosmetic deception vectors (e.g., attacker creates a malicious website that looks like and behaves like the legitimate one). The most common types of social semantic attacks include phishing, spamming, defacement, and malware attacks. We investigate the feasibility of developing URL-based social semantic attack detection models utilizing character-aware language models. Specifically, we developed three types of models: long short-term memory (LSTM)-based detection model, convolutional neural network (CNN)-based detection model, and CharacterBERT-based detection model. We benchmarked performances of different models for different attacks. Using the characterBERT-based detection model, the overall evaluation recorded a high detection accuracy of 99.65% by averaging the results of performing a 5-fold cross-validation. Considering the model performance per class, the CharacterBERT model ranked the best model among our 3 models in detecting the social semantic attacks, reaching best accuracy of 99.90% in detecting defacement attack.
Editor:
IEEE
Idioma:
Inglês
This feature requires javascript
This feature requires javascript
Voltar para lista de resultados
Anterior
Resultado
8
Avançar
This feature requires javascript
This feature requires javascript
Buscando em bases de dados remotas. Favor aguardar.
Buscando por
em
scope:(USP_PRODUCAO),scope:(USP_EBOOKS),scope:("PRIMO"),scope:(USP),scope:(USP_EREVISTAS),scope:(USP_FISICO),primo_central_multiple_fe
Mostrar o que foi encontrado até o momento
This feature requires javascript
This feature requires javascript