AI-Based Classification Models for Phishing and Intrusion Detection

In the modern digital era, phishing and intrusion attacks pose significant threats to cybersecurity. Traditional detection methods struggle to keep pace with the evolving attack landscape. This paper presents an AI driven approach utilizing classification models to detect phishing attempts and intrusions effectively. Various machine learning algorithms, including Logistic Regression, Support Vector Machines (SVM), Decision Trees, and Random Forests, are explored to enhance detection accuracy. The proposed model integrates Natural Language Processing (NLP) techniques for improved phishing identification. Performance evaluation is conducted using key metrics such as accuracy, precision, and recall. Experimental results demonstrate the effectiveness of the AI-based approach in mitigating cyber threats and improving digital security.