Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Spam and Phishing Detection · Network Security and Intrusion Detection · Sentiment Analysis and Opinion Mining
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia Kuala Lumpur, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Malaysia
School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 80000, Johor, Malaysia
Media and Games Center of Excellence (MagicX), Universiti Teknologi Malaysia, Skudai, Johor Bahru 81310, Johor, Malaysia
Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Rokitanskeho 62, 500 03 Hradec Kralove, Czech Republic
Tokyo Metropolitan College of Industrial Technology, Tokyo 140-0011, Japan
Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18001 Granada, Spain
i-SOMET Incorporated Association, Morioka 020-0000, Japan
Regional Research Center, Iwate Prefectural University, Iwate 028-4211, Japan
Funding
Ministry of Higher Education under the Fundamental Research · Universiti Teknologi Malaysia (UTM) · Malaysia Re-search University Network (MRUN) · Faculty of Informatics and Management, University of Hradec Kralove, SPEV project
References (65)
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