Arar Al Tawil^1,*, Laiali Almazaydeh², Doaa Qawasmeh³, Baraah Qawasmeh⁴, Mohammad Alshinwan^1,5, Khaled Elleithy⁶

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3395-3412, 2024, DOI:10.32604/cmc.2024.057279 - 18 November 2024

Abstract Cybercriminals often use fraudulent emails and fictitious email accounts to deceive individuals into disclosing confidential information, a practice known as phishing. This study utilizes three distinct methodologies, Term Frequency-Inverse Document Frequency, Word2Vec, and Bidirectional Encoder Representations from Transformers, to evaluate the effectiveness of various machine learning algorithms in detecting phishing attacks. The study uses feature extraction methods to assess the performance of Logistic Regression, Decision Tree, Random Forest, and Multilayer Perceptron algorithms. The best results for each classifier using Term Frequency-Inverse Document Frequency were Multilayer Perceptron (Precision: 0.98, Recall: 0.98, F1-score: 0.98, Accuracy: 0.98). Word2Vec’s More >

Md. Anwar Hussen Wadud¹, M. F. Mridha¹, Jungpil Shin^2,*, Kamruddin Nur³, Aloke Kumar Saha⁴

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1775-1791, 2023, DOI:10.32604/csse.2023.027841 - 15 June 2022

Abstract Offensive messages on social media, have recently been frequently used to harass and criticize people. In recent studies, many promising algorithms have been developed to identify offensive texts. Most algorithms analyze text in a unidirectional manner, where a bidirectional method can maximize performance results and capture semantic and contextual information in sentences. In addition, there are many separate models for identifying offensive texts based on monolingual and multilingual, but there are a few models that can detect both monolingual and multilingual-based offensive texts. In this study, a detection system has been developed for both monolingual… More >