An Automated Framework for Real-time Phishing URL Detection

Sadique, Farhan; Kaul, Raghav; Badsha, Shahriar; Sengupta, Shamik

doi:10.1109/ccwc47524.2020.9031269

Cited by 24 publications

(6 citation statements)

References 15 publications

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“…Furthermore, some recent work on phishing detection has been conducted using the same PhishTank data set, as shown in Table 8. The table includes six different approaches, including KNN [37], SVM [40], random forest classifier [39], RNN [1], NLP [38], and the proposed model, which is a 1D CNN. The accuracy values for each approach are shown alongside the corresponding dataset used for training and testing.…”

Section: Discussionmentioning

confidence: 99%

A Deep Learning-Based Innovative Technique for Phishing Detection in Modern Security with Uniform Resource Locators

Aldakheel

Zakariah

Gashgari

et al. 2023

Sensors

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Organizations and individuals worldwide are becoming increasingly vulnerable to cyberattacks as phishing continues to grow and the number of phishing websites grows. As a result, improved cyber defense necessitates more effective phishing detection (PD). In this paper, we introduce a novel method for detecting phishing sites with high accuracy. Our approach utilizes a Convolution Neural Network (CNN)-based model for precise classification that effectively distinguishes legitimate websites from phishing websites. We evaluate the performance of our model on the PhishTank dataset, which is a widely used dataset for detecting phishing websites based solely on Uniform Resource Locators (URL) features. Our approach presents a unique contribution to the field of phishing detection by achieving high accuracy rates and outperforming previous state-of-the-art models. Experiment results revealed that our proposed method performs well in terms of accuracy and its false-positive rate. We created a real data set by crawling 10,000 phishing URLs from PhishTank and 10,000 legitimate websites and then ran experiments using standard evaluation metrics on the data sets. This approach is founded on integrated and deep learning (DL). The CNN-based model can distinguish phishing websites from legitimate websites with a high degree of accuracy. When binary-categorical loss and the Adam optimizer are used, the accuracy of the k-nearest neighbors (KNN), Natural Language Processing (NLP), Recurrent Neural Network (RNN), and Random Forest (RF) models is 87%, 97.98%, 97.4% and 94.26%, respectively, in contrast to previous publications. Our model outperformed previous works due to several factors, including the use of more layers and larger training sizes, and the extraction of additional features from the PhishTank dataset. Specifically, our proposed model comprises seven layers, starting with the input layer and progressing to the seventh, which incorporates a layer with pooling, convolutional, linear 1 and 2, and linear six layers as the output layers. These design choices contribute to the high accuracy of our model, which achieved a 98.77% accuracy rate.

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Section: Discussionmentioning

confidence: 99%

A Deep Learning-Based Innovative Technique for Phishing Detection in Modern Security with Uniform Resource Locators

Aldakheel

Zakariah

Gashgari

et al. 2023

Sensors

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“…Despite the potential benefits of this process, our literature review has highlighted that some papers analyze the importance of the extracted features as part of the training process (see, e.g. [71], [78], [81], [90], [108], [109]), while only few papers analyze features with the objective of retaining the most representative ones (see, e.g., [82], [91], [110], [111], [112], [113], [114], [115]).…”

Section: B Feature Selectionmentioning

confidence: 99%

Phishing or Not Phishing? A Survey on the Detection of Phishing Websites

2023

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Phishing is a security threat with serious effects on individuals as well as on the targeted brands. Although this threat has been around for quite a long time, it is still very active and successful. In fact, the tactics used by attackers have been evolving continuously in the years to make the attacks more convincing and effective. In this context, phishing detection is of primary importance. The literature offers many diverse solutions that cope with this issue and in particular with the detection of phishing websites. This paper provides a broad and comprehensive review of the state of the art in this field by discussing the main challenges and findings. More specifically, the discussion is centered around three important categories of detection approaches, namely, list-based, similarity-based and machine learning-based. For each category we describe the detection methods proposed in the literature together with the datasets considered for their assessment and we discuss some research gaps that need to be filled.

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“…Wadas [75] presented a model to detect phishing URLs using ML techniques. The datasets used to train the model are from PhishTank [14], and another dataset was adapted from the author's previous work [76]. Lexical and network-based features are extracted in this model by a total of 14 features.…”

Section: ) Lexical and Network-based Features Studiesmentioning

confidence: 99%

Detecting Malicious URLs Using Machine Learning Techniques: Review and Research Directions

et al. 2022

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In recent years, the digital world has advanced significantly, particularly on the Internet, which is critical given that many of our activities are now conducted online. As a result of attackers' inventive techniques, the risk of a cyberattack is rising rapidly. One of the most critical attacks is the malicious URL intended to extract unsolicited information by mainly tricking inexperienced end users, resulting in compromising the user's system and causing losses of billions of dollars each year. As a result, securing websites is becoming more critical. In this paper, we provide an extensive literature review highlighting the main techniques used to detect malicious URLs that are based on machine learning models, taking into consideration the limitations in the literature, detection technologies, feature types, and the datasets used. Moreover, due to the lack of studies related to malicious Arabic website detection, we highlight the directions of studies in this context. Finally, as a result of the analysis that we conducted on the selected studies, we present challenges that might degrade the quality of malicious URL detectors, along with possible solutions.

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An Automated Framework for Real-time Phishing URL Detection

Cited by 24 publications

References 15 publications

A Deep Learning-Based Innovative Technique for Phishing Detection in Modern Security with Uniform Resource Locators

A Deep Learning-Based Innovative Technique for Phishing Detection in Modern Security with Uniform Resource Locators

Phishing or Not Phishing? A Survey on the Detection of Phishing Websites

Detecting Malicious URLs Using Machine Learning Techniques: Review and Research Directions

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