Earticle

초록

영어

The extensive use of technology and the internet in the modern digital age has enhanced our lives but also generated serious security risks, with phishing being one of the most common cybercrimes. Phishing attempts to get personal information by spoofing trustworthy websites and taking advantage of private information such as usernames, passwords, and account IDs. Researchers are using deep learning and machine learning approaches to tackle this problem. These methods are used in our study to identify phishing websites using a dataset of 48 characteristics and 10,000 occurrences, of which 5,000 are phishing and 5,000 are legal websites. We evaluated four deep learning models (ANN, LSTM, BiLSTM, and a hybrid ANN-LSTM model) and five machine learning models (Decision Tree, k-Nearest Neighbor, Naive Bayes, Logistic Regression, SVM) to assess their performance using evaluates for accuracy, F1 score, recall, and precision. Because of the drawbacks of adopting a small value, k-Nearest Neighbor fared the lowest, with 74% accuracy, while the hybrid ANNLSTM model outperformed the other models, with a maximum accuracy of 98%. Our results imply that deep learning models, especially hybrid ones, offer better phishing website detection capabilities.

목차

Abstract
I. INTRODUCTION
II. LITERATURE REVIEW
III. DATASET
IV. METHODOLOGY
A. ANN-LSTM Model
B. LSTM
C. Artificial Neural Network (ANN)
V. EXPERIMENTS AND RESULTS
A. Environment Setup
B. Evaluation Matrices
C. Evaluation of Results
VI. ANALYSIS OF NETWORK STRUCTURE AND PERFORMANCE
VII. CONCLUSION
REFERENCES

저자

• Abdullah [ Department of Computer Sciences Bahria University, Lahore Campus Lahore, Punjab 54600, Pakistan ]
• Muhammad Asif [ Department of Information Sciences University of Education Lahore, Pakistan ]
• Sagheer Abbas [ Department of Computer Science, Prince Mohammad Bin Fahd University, Al Khobar, Dhahran,34754, Saudi Arabia. ]
• Muhammad Adnan Khan [ School of Computing, Skyline University College, Sharjah, UAE; RISC, Riphah International University, Lahore, Pakistan. ]
• Mayraj Fatima [ National College of Business Administration and Economics Lahore, Pakistan ]
• Atif Ali [ Research Management Centre (RMC), Multimedia University, Cyberjaye 63100 Malaysia. ]

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