Malicious web content detection using machine leaning

Desai, Arpan; Jatakia, Janvi; Naik, Krishna; Raul, Nataasha

doi:10.1109/rteict.2017.8256834

Cited by 18 publications

(16 citation statements)

References 15 publications

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“…Ankit and Gupta extracted features from URLs and source code from users' side and adopted Random Forest as the classifier [18]. Desai et al selected 22 features such as URL length and Google Index and adopted SVM, K Nearest-Neighbor (KNN) and Random Forest as the classifier [16]. Gupta and Sachdeva used SVM to detect malicious URLs in Facebook [17].…”

Section: B Classical Machine Learning Techniquesmentioning

confidence: 99%

CNN Based Malicious Website Detection by Invalidating Multiple Web Spams

Liu

Lee

2020

IEEE Access

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Although a variety of techniques to detect malicious websites have been proposed, it becomes more and more difficult for those methods to provide a satisfying result nowadays. Many malicious websites can still escape detection with various Web spam techniques. In this paper, we first summarize three types of Web spam techniques used by malicious websites, such as redirection spam, hidden IFrame spam, and content hiding spam. We then present a new detection method that adopts the perspective of users and takes screenshots of malicious webpages to invalidate Web spams. The proposed detection method uses a Convolutional Neural Network, which is a class of deep neural networks, as a classification algorithm. In order to verify the effectiveness of the method, two different experiments have been conducted. First, the proposed method was tested based on a constructed complex dataset. We present comparison results between the proposed method and representative machine learning-based detection algorithms. Second, the proposed method was tested to detect malicious websites in a real-world Web environment for three months. These experimental results illustrate that the proposed method has a better performance and is applicable to a practical Web environment. INDEX TERMS Convolutional neural network, machine learning, malicious website detection.

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Section: B Classical Machine Learning Techniquesmentioning

confidence: 99%

CNN Based Malicious Website Detection by Invalidating Multiple Web Spams

Liu

Lee

2020

IEEE Access

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“…Authors [11] in this paper created an extension to Google Chrome to detect phishing websites content with the help of machine learning algorithms. Dataset UCI-Machine Learning Repository used and 22 features were extracted for this dataset.…”

Section: Literary Reviewmentioning

confidence: 99%

“…Algorithms kNN, SVM and Random Forest were chosen for precision, recall,f1-score and accuracy comparison. Random Forest obtained a best score and HTML,JavaScript, CSS [11] used for implementing chrome extension along with python. This extension is having a drawback of declared malicious site list which is increasing every day.…”

Section: Literary Reviewmentioning

confidence: 99%

Phishing Websites Detection using Machine Learning

2019

IJRTE

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Phishing is a common attack on credulous people by making them to disclose their unique information using counterfeit websites. The objective of phishing website URLs is to purloin the personal information like user name, passwords and online banking transactions. Phishers use the websites which are visually and semantically similar to those real websites. As technology continues to grow, phishing techniques started to progress rapidly and this needs to be prevented by using anti-phishing mechanisms to detect phishing. Machine learning is a powerful tool used to strive against phishing attacks. This paper surveys the features used for detection and detection techniques using machine learning

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“…Outras abordagens comumente utilizadas são baseadas em processamento de linguagem natural [Sahingoz et al 2019], parâmetros do domínio em questão [Desai et al 2017] e análise do conteúdo da página [Jain and Gupta 2018]. Vale notar que essas técnicas podem não alcançar uma boa generalização para detecção de novos endereços na Internet.…”

Section: Figura 1 Sequência De Passos Para Um Ataque De Phishing Bem Sucedidounclassified

“…Já a proposta de [Desai et al 2017] apresenta uma extensão de navegador para detecção de phishing utilizando machine learning. Os autores utilizaram um dataset público da Universidade da Califórnia em Irvine (UCI) 4 composto por 11.055 URLs classificadas como phishing ou não, onde cada endereço tem 30 parâmetros para efeito de classificação.…”

Section: Trabalhos Relacionadosunclassified

PhishKiller: Uma Ferramenta para Detecção e Mitigação de Ataques de Phishing Através de Técnicas de Deep Learning

Souza¹,

Lemos²,

Silva³

et al. 2019

Anais Estendidos Do XIX Simpósio Brasileiro De Segurança Da Informação E De Sistemas Computacionais (SBSeg Estendido 2019)

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A expansão da Internet expandiu as possibilidades de ações fraudulentas. Entre essas ações, destaca-se a atividade de phishing, criada com o objetivo de capturar as credenciais do usuário através de uma página falsa similar à do serviço original. Diante disso, este trabalho propõe o PhishKiller, uma ferramenta capaz de detectar e mitigar ataques de phishing, utilizando um proxy para interceptação dos endereços acessados pelo usuário e técnicas de deep learning não baseadas em parâmetros para classiﬁcação dos URLs. As avaliações demonstraram a viabilidade do PhishKiller, que teve uma acurácia de 98.3% na detecção de endereços maliciosos e tempo de processamento médio de 81.68ms por requisição.

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Malicious web content detection using machine leaning

Cited by 18 publications

References 15 publications

CNN Based Malicious Website Detection by Invalidating Multiple Web Spams

CNN Based Malicious Website Detection by Invalidating Multiple Web Spams

Phishing Websites Detection using Machine Learning

PhishKiller: Uma Ferramenta para Detecção e Mitigação de Ataques de Phishing Através de Técnicas de Deep Learning

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