PDMLP: Phishing Detection using Multilayer Perceptron

Al-Ahmadi, Saad; Lasloum, Tariq

doi:10.5121/ijnsa.2020.12304

Cited by 12 publications

(13 citation statements)

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“…However, four different DL architectures from various categories (supervised, unsupervised, and hybrid) were implemented in our research work. Moreover, dropout rate and batch size, which were not specified in some studies [6,18,28], were included in our empirical analysis. In addition, it is also observed from the table that although different authors used the same algorithm, their optimal set of parameters and accuracy results were not the same.…”

Section: Results With Grumentioning

confidence: 99%

“…It is considered one of the important parameters in DL architectures that determines the training model's output, accuracy, and efficiency. In the neural network, neurons of the same layer usually use the same activation function [6]. Rectified Linear Unit (ReLU), Softmax, sigmoid, and Tanh are examples of frequently-used activation functions for DL models.…”

Section: Hyper-parametersmentioning

confidence: 99%

“…In addition, Precision, Recall, F1-Score, Area Under the Curve, and Accuracy are also important metrics that are required in the performance evaluation of phishing detection models. The formula of these metrics can be defined as Equations (1)- (6). In any phishing detection model, the primary purpose is to identify phishing attacks; therefore, a phishing sample is often regarded as a positive instance while a legitimate sample is considered as a negative instance [3,41,51].…”

Section: Performance Metricsmentioning

confidence: 99%

“…Positive correlations mean one feature marks the URL as phishing, and so does the other. Whereas negative correlations mean one feature marks the URL as malicious, while the other does not [6].…”

Section: Website Featuresmentioning

confidence: 99%

“…Numerous researchers in cyber security have shifted their attention towards DL algorithms. Notably, researchers and security experts have also recognized its significance in the phishing detection domain [6][7][8]. During the last few years, website phishing has become one of the most common phishing attacks in cyberspace.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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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Section: Results With Grumentioning

confidence: 99%

Section: Hyper-parametersmentioning

confidence: 99%

Section: Performance Metricsmentioning

confidence: 99%

Section: Website Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Selamat

Krejcar

et al. 2021

Applied Sciences

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Rotation Forest-Based Logistic Model Tree for Website Phishing Detection

Balogun

Oluwatobi

Usman-Hamza

et al. 2021

Lecture Notes in Computer Science

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Applications of deep learning for phishing detection: a systematic literature review

Çatal

Giray²,

Tekinerdoğan

et al. 2022

Knowl Inf Syst

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Phishing attacks aim to steal confidential information using sophisticated methods, techniques, and tools such as phishing through content injection, social engineering, online social networks, and mobile applications. To avoid and mitigate the risks of these attacks, several phishing detection approaches were developed, among which deep learning algorithms provided promising results. However, the results and the corresponding lessons learned are fragmented over many different studies and there is a lack of a systematic overview of the use of deep learning algorithms in phishing detection. Hence, we performed a systematic literature review (SLR) to identify, assess, and synthesize the results on deep learning approaches for phishing detection as reported by the selected scientific publications. We address nine research questions and provide an overview of how deep learning algorithms have been used for phishing detection from several aspects. In total, 43 journal articles were selected from electronic databases to derive the answers for the defined research questions. Our SLR study shows that except for one study, all the provided models applied supervised deep learning algorithms. The widely used data sources were URL-related data, third party information on the website, website content-related data, and email. The most used deep learning algorithms were deep neural networks (DNN), convolutional neural networks, and recurrent neural networks/long short-term memory networks. DNN and hybrid deep learning algorithms provided the best performance among other deep learning-based algorithms. 72% of the studies did not apply any feature selection algorithm to build the prediction model. PhishTank was the most used dataset among other datasets. While Keras and Tensorflow were the most preferred deep learning frameworks, 46% of the articles did not mention any framework. This study also highlights several challenges for phishing detection to pave the way for further research.

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PDMLP: Phishing Detection using Multilayer Perceptron

Cited by 12 publications

References 0 publications

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

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

Rotation Forest-Based Logistic Model Tree for Website Phishing Detection

Applications of deep learning for phishing detection: a systematic literature review

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