Evaluation of Phishing Techniques Based on Machine Learning

Merlin. V. Kunju, Esther Dainel, Heron Celestie Anthony, Sonali Bhelwa
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引用次数: 11

Abstract

Phishing sites is the major problems for online security challenges because of large number of online transactions is done every day. The objective of the paper is to do survey about the phishing: A social attack and its detection and to make aware of the users who doesn’t know about this major attack as many of them are still falling in the trap. Most of the users are unaware about this problem; they unknowingly fill many forms that belong to phishing website which are hidden. This leads to the leaking of sensitive information of the victim. This study also gives brief knowledge about several machine learning techniques such as kNN Algorithm, Naïve Bayes, Decision Tree, Support Vector Machines, Neural Network and Random Forest algorithm for predicting phishing sites.
基于机器学习的网络钓鱼技术评价
由于每天都有大量的在线交易,网络钓鱼网站是网络安全挑战的主要问题。本文的目的是调查网络钓鱼:一种社会攻击及其检测,并了解那些不知道这种主要攻击的用户,因为他们中的许多人仍然落入陷阱。大多数用户都没有意识到这个问题;他们在不知情的情况下填写了许多属于钓鱼网站的表格,这些表格是隐藏的。这导致了受害者敏感信息的泄露。本研究还简要介绍了几种机器学习技术,如kNN算法,Naïve贝叶斯,决策树,支持向量机,神经网络和随机森林算法,用于预测网络钓鱼网站。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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