Scholarlink

This research paper investigates the integration of Artificial Intelligence (AI), with a focus on Machine Learning (ML) and Deep Learning (DL), for bolstering cybersecurity defences against phishing attacks. Utilizing a comprehensive dataset of URL features, the study assesses the efficacy of various ML algorithms—namely Decision Tree, Logistic Regression, Support Vector Machine, Random Forest, and K-Nearest Neighbors—in pinpointing phishing websites. Research paper is conducted using Google Colaboratory, Python libraries and Weka tool. The research identifies the Random Forest algorithm as the most effective, demonstrating superior accuracy in detecting phishing URLs during both training and testing phases. The findings accentuate the pivotal role of AI in advancing cybersecurity measures, advocating for the incorporation of sophisticated AI technologies in the fight against cyber threats. Additionally, it outlines future research directions, including the enhancement of model precision through the integration of more comprehensive data attributes. This paper significantly contributes to the cybersecurity and AI domains by showcasing the practical applications and benefits of AI in identifying and mitigating cyber risks.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An Empirical Analysis of Machine Learning Approaches for Phishing Detection

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Ivan Cvitić,
  • Hussam Al-Hamadi,
  • Tibor Mijo Kuljanić,
  • David Aleksić

摘要

This research paper investigates the integration of Artificial Intelligence (AI), with a focus on Machine Learning (ML) and Deep Learning (DL), for bolstering cybersecurity defences against phishing attacks. Utilizing a comprehensive dataset of URL features, the study assesses the efficacy of various ML algorithms—namely Decision Tree, Logistic Regression, Support Vector Machine, Random Forest, and K-Nearest Neighbors—in pinpointing phishing websites. Research paper is conducted using Google Colaboratory, Python libraries and Weka tool. The research identifies the Random Forest algorithm as the most effective, demonstrating superior accuracy in detecting phishing URLs during both training and testing phases. The findings accentuate the pivotal role of AI in advancing cybersecurity measures, advocating for the incorporation of sophisticated AI technologies in the fight against cyber threats. Additionally, it outlines future research directions, including the enhancement of model precision through the integration of more comprehensive data attributes. This paper significantly contributes to the cybersecurity and AI domains by showcasing the practical applications and benefits of AI in identifying and mitigating cyber risks.