PhishDump: A multi-model ensemble based technique for the detection of phishing sites in mobile devices

Rao, Routhu Srinivasa; Vaishnavi, Tatti; Pais, Alwyn Roshan

doi:10.1016/j.pmcj.2019.101084

Cited by 34 publications

(28 citation statements)

References 27 publications

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“…Rao et al [ 55 ] proposed a mobile application called PhishDump to categorize legitimate and phishing websites on mobile devices. PhishDump works with multiple models using the long short-term memory (LSTM) and support vector machine (SVM) classifier.…”

Section: Related Workmentioning

confidence: 99%

A hybrid DNN–LSTM model for detecting phishing URLs

Özcan

Çatal

Dönmez

et al. 2021

Neural Comput & Applic

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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.

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Section: Related Workmentioning

confidence: 99%

A hybrid DNN–LSTM model for detecting phishing URLs

Özcan

Çatal

Dönmez

et al. 2021

Neural Comput & Applic

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“…However, architectures based on deep learning models usually introduce the following problems. First of all, deep learning algorithms have high hardware requirements and a lot of calculations, so they cannot be configured on mobile portable devices 4,28 . Secondly, due to the complexity of the deep learning model, it is difficult to design a deep learning model that meets the specified application scenarios.…”

Section: Future Discussionmentioning

confidence: 99%

“…The static analysis technology based on the features of PC web pages has been widely used. It should be noted that phishing is one of the most serious threats in current mobile and social networks 3,6,28 . To mitigate potential phishing attacks on PC‐side web pages, some effective solutions have been proposed in recent years, including e‐mail filtering, 29 using content‐based features, 30 blacklist, 11‐13 and deployability and usability in real world 22 .…”

Section: Background and Related Workmentioning

confidence: 99%

MMWD: An efficient mobile malicious webpage detection framework based on deep learning and edge cloud

Liu

Zhu

et al. 2021

Concurrency and Computation

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In recent years, with the rapid development of mobile social networks and services, the research of mobile malicious webpage detection has become a hot topic. Most of the existing malicious webpage detection systems are deployed on desktop systems and servers. Due to the limitation of network transmission delay and computing resources, these existing solutions fail to provide the real‐time and lightweight properties for mobile webpage detection. In this paper, we propose an advanced mobile malicious webpage detection framework based on deep learning and edge cloud. Inspired by the idea of edge computing, a multidevice load optimization approach is first introduced to improve detection efficiency. Second, an automatic extraction approach based on deep learning model features is presented to enhance detection accuracy. Furthermore, detection systems can be flexibly deployed on edge nodes and servers, thus providing the properties of resource optimization deployment and real‐time detection. Finally, comparative analysis and performance evaluation are presented to show the detection efficiency and accuracy of the proposed framework.

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“…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].…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

“…Other measures training time, detection time, error rate, detection cost, number of epochs per second, etc. [33,42,46,47].…”

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

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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.

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PhishDump: A multi-model ensemble based technique for the detection of phishing sites in mobile devices

Cited by 34 publications

References 27 publications

A hybrid DNN–LSTM model for detecting phishing URLs

A hybrid DNN–LSTM model for detecting phishing URLs

MMWD: An efficient mobile malicious webpage detection framework based on deep learning and edge cloud

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

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