Efficient deep learning techniques for the detection of phishing websites

M., Somesha; Pais, Alwyn Roshan; Rao, Routhu Srinivasa; Rathour, Vikram Singh

doi:10.1007/s12046-020-01392-4

Cited by 73 publications

(40 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To create a mixed classification model, CNN and LSTM algorithms were used. [ 61 ] proposed a deep learning model to determine the legitimacy of a website. URL heuristics and third-party service-based features were used to train deep learning models.…”

Section: Related Workmentioning

confidence: 99%

A hybrid DNN–LSTM model for detecting phishing URLs

Özcan

Çatal

Dönmez

et al. 2021

Neural Comput & Applic

View full text Add to dashboard Cite

Phishing 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.

show abstract

Section: Related Workmentioning

confidence: 99%

A hybrid DNN–LSTM model for detecting phishing URLs

Özcan

Çatal

Dönmez

et al. 2021

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…Similar to DNN and CNN, LSTM can be implemented individually [20,[41][42][43][44][45], incorporated with traditional machine learning techniques [46,47], or combined with other DL algorithms in a hybrid model for an improved performance in detecting malicious websites [10,11,31,33,35,36]. Among the studies of LSTM-based phishing detection models, a majority of them specified the parameter settings for neural network architecture, number of epochs, and learning rate; but ignored the dropout rate and batch size [31,41,42,44,47]. Moreover, only certain parameters were optimized during the fine-tuning process [32,42].…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

“…Among the studies of LSTM-based phishing detection models, a majority of them specified the parameter settings for neural network architecture, number of epochs, and learning rate; but ignored the dropout rate and batch size [31,41,42,44,47]. Moreover, only certain parameters were optimized during the fine-tuning process [32,42]. To evaluate the overall performance of LSTM models, four popular metrics were used, being accuracy, precision, recall, and F1-score [31,35,41,44,45].…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study

Selamat

Krejcar

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

Phishing detection with high-performance accuracy and low computational complexity has always been a topic of great interest. New technologies have been developed to improve the phishing detection rate and reduce computational constraints in recent years. However, one solution is insufficient to address all problems caused by attackers in cyberspace. Therefore, the primary objective of this paper is to analyze the performance of various deep learning algorithms in detecting phishing activities. This analysis will help organizations or individuals select and adopt the proper solution according to their technological needs and specific applications’ requirements to fight against phishing attacks. In this regard, an empirical study was conducted using four different deep learning algorithms, including deep neural network (DNN), convolutional neural network (CNN), Long Short-Term Memory (LSTM), and gated recurrent unit (GRU). To analyze the behaviors of these deep learning architectures, extensive experiments were carried out to examine the impact of parameter tuning on the performance accuracy of the deep learning models. In addition, various performance metrics were measured to evaluate the effectiveness and feasibility of DL models in detecting phishing activities. The results obtained from the experiments showed that no single DL algorithm achieved the best measures across all performance metrics. The empirical findings from this paper also manifest several issues and suggest future research directions related to deep learning in the phishing detection domain.

show abstract

“…Machine learning can provide the ability to prevent against even zero-day phishing attacks if provided with enough training data. This methodology, although powerful, is highly dependent on the size and quality of the training dataset and the fine tuning of hyperparameters to obtain optimal accuracy [77].…”

Section: Machine Learningmentioning

confidence: 99%

Phishing Attacks Survey: Types, Vectors, and Technical Approaches

Alabdan

2020

Future Internet

View full text Add to dashboard Cite

Phishing attacks, which have existed for several decades and continue to be a major problem today, constitute a severe threat in the cyber world. Attackers are adopting multiple new and creative methods through which to conduct phishing attacks, which are growing rapidly. Therefore, there is a need to conduct a comprehensive review of past and current phishing approaches. In this paper, a review of the approaches used during phishing attacks is presented. This paper comprises a literature review, followed by a comprehensive examination of the characteristics of the existing classic, modern, and cutting-edge phishing attack techniques. The aims of this paper are to build awareness of phishing techniques, educate individuals about these attacks, and encourage the use of phishing prevention techniques, in addition to encouraging discourse among the professional community about this topic.

show abstract

Efficient deep learning techniques for the detection of phishing websites

Cited by 73 publications

References 36 publications

A hybrid DNN–LSTM model for detecting phishing URLs

A hybrid DNN–LSTM model for detecting phishing URLs

Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study

Phishing Attacks Survey: Types, Vectors, and Technical Approaches

Contact Info

Product

Resources

About