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

Abstract

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.

On the use of deep learning in software defect prediction

“…Like we can observe in some other domains, like malware detection (Catal et al, 2021) and phishing detection (Catal et al, 2022), CNN, RNN/LSTM/GRU, and MLP are the top three most frequently used DL approaches. The overall reason may be that these algorithms performed well in many tasks, and they are well-known among researchers and practitioners; this fact is indeed mentioned in numerous works covered in this study.…”

Machine Learning Techniques for Detecting Phishing URL Attacks

“…Security companies provide solutions for users to manage malicious activities. PhishMe develops software for organization security workers to deal with phishing attacks just by clicking on a button provided in the E-Mail client Add-in [31].…”

Applications of Deep Learning for Drug Discovery Systems with BigData

“…The amounts of data and computational power required for learning have increased. Deep learning uses DNNs with hundreds of layers and a large number of parameters related to structure [145][146][147][148][149][150][151][152][153][154][155][156][157][158][159][160][161]. Therefore, it is prone to overfitting, which is a condition where the learning data are overfitted, generalization is not possible, and high accuracy cannot be achieved with unknown data.…”