Phishing Attack Identification using Hybrid ML Models
Keywords:
Cybersecurity, Deep learning, Hybrid models, Machine learning, Phishing detection, URL analysisAbstract
A phishing attack is a type of threat where attackers steal peoples personal and financial information for bad purposes. Many strategies have been used to stop cyber threats and find patterns in data. Machine Learning and Deep Learning are very important in this area. Some models like Long Short-Term Memory (LSTM), Support Vector Machine (SVM) and Convolutional Neural Networks (CNN) are often used to detect and prevent phishing attacks and other cybersecurity threats. This research provides a method for identifying phishing emails using several machine learning algorithms, such as Support Vector Machine and Logistic Regression (LR). Two different datasets were used in this research. The important features were identified, and they included a mix of content-based, URL lexical-based and domain-based attributes. Then various machine learning models were. Their performance was compared. The results showed that feature selection is crucial in improving the performance of the models. The study also tried to find the important features that help detect phishing websites accurately. The experimental results show that optimized preprocessing and feature selection greatly improve the effectiveness of phishing email detection systems. Phishing attacks are a problem, and using machine learning algorithms and feature selection can help prevent them. Machine Learning and Deep Learning are key to stopping these attacks. Phishing emails and websites can be detected using these methods. The findings of this research can help improve cybersecurity.
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