• Corpus ID: 232335391

Detecting Phishing Sites -- An Overview

  title={Detecting Phishing Sites -- An Overview},
  author={P.Kalaharsha and Bluetooth Security and Institute for Development and Research in Banking Technology and Hyderabad and India and School of Materials Science and Information Sciences and University of Hyderabad},
Phishing is one of the most severe cyber-attacks where researchers are interested to find a solution. In phishing, attackers lure end-users and steal their personal information. To minimize the damage caused by phishing must be detected as early as possible. There are various phishing attacks like spear phishing, whaling, vishing, smishing, pharming and so on. There are various phishing detection techniques based on whitelist, black-list, content-based, URL-based, visualsimilarity and machine… 

Figures and Tables from this paper

URL Based Phishing Website Detector

This model is strapped with a web application and web extension which acts as front-end interface and it has successfully defended an email-based phishing attack simulation in detecting a phishing website.

A survey of phishing detection: from an intelligent countermeasures view

  • Yifei Wang
  • Computer Science
    2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)
  • 2022
All kinds of phishing are classified, including E-mail phishing and search engine phishing, and existing and widely used detection methods are divided into seven categories such as list-based heuristic machine learning, and introduces them in detail.

A Survey of Machine Learning-Based Solutions for Phishing Website Detection

A detailed comparison of various solutions for phishing website detection is provided, starting with the life cycle of phishing, and introduces common anti-phishing methods, which mainly focuses on the method of identifying phishing links.

An adaptive approach for internet phishing detection based on log data

The proposed system for this paper includes efficient data extraction from the web file through data collection and preprocessing, and feature-extracting URL analysis to detect website phishing addresses, which results in a classification algorithm being applied to determine if website addresses are phishing or legitimate.

Analysis of email phishing in session hijacking

The purpose of this study is to provide information on how to identify an infected email and educate users about the features of phishing emails, based on the layout ofphishing concepts and knowing how a phishing attack occurs.

Fraud usage detection in internet users based on log data

The proposed system in this paper includes efficient data extraction from the web file through data collection and preprocessing, and feature-extracting URL analysis to detect website phishing addresses, which shows the robustness of the proposed system.

Smishing Strategy Dynamics and Evolving Botnet Activities in Japan

Frida’s hooking capability was employed to decode the upper layers (WebSocket and JSON-RPC) to create a list of all commands flowing over the botnet channel, and the proposed malicious domain detection method exploited the tendency of the attackers to create domains in batches.

Deep Learning Based Sentiment Analysis for Phishing SMS Detection

This chapter is based on a discussion and comparison of various classification models that are used for phishing SMS detection through sentiment analysis and CNN showed the highest accuracy of 99.47% as a classification model.

Systematic Literature Review: Anti-Phishing Defences and Their Application to Before-the-click Phishing Email Detection

This paper discusses the performance and suitability of using these techniques for detecting phishing emails before the end-user even reads the email, and suggests some promising areas for further research.



Proactive Phishing Sites Detection

A new approach to the detection of zero-hour phishing sites: proactive detection so that malicious sites are detected as early as possible, shutdown by the specialized agencies and mitigation of user damages are expected.

Phishing Website Detection Based on Machine Learning: A Survey

  • Charu SinghMeenu
  • Computer Science
    2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS)
  • 2020
The review creates awareness ofphishing attacks, detection of phishing attacks and encourages the practice of phishers prevention among the readers.

Variants of phishing attacks and their detection techniques

A framework to detect and prevent phishing attacks is proposed and a combination of supervised and unsupervised machine learning techniques is used to detect known and unknown attacks.

Towards the Detection of Phishing Attacks

  • A. APraveen K
  • Computer Science
    2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184)
  • 2020
This review raises awareness of those phishing strategies and helps the user to practice phishing prevention and a hybrid approach of phishing detection also described having fast response time and high accuracy.

Fresh-Phish: A Framework for Auto-Detection of Phishing Websites

This work develops a framework, called ""Fresh-Phish"", for creating current machine learning data for phishing websites, and analyzes not just the accuracy of the technique, but also how long it takes to train the model.

Phishing Website Classification and Detection Using Machine Learning

This paper has compared different machine learning techniques for the phishing URL classification task and achieved the highest accuracy of 98% for Naïve Bayes Classifier with a precision=1, recall = .95 and F1-Score= .97.

An Adaptive Machine Learning Based Approach for Phishing Detection Using Hybrid Features

This work develops a reliable detection system which can adaptively match the changing environment and phishing websites and does not require any service from the third-party.

Efficient deep learning techniques for the detection of phishing websites

Novel phishing URL detection models using Deep Neural Network, Long Short-Term Memory, and Convolution Neural Network are proposed using only 10 features of earlier work, which achieves an accuracy of 99.52% for DNN, 99.57% for LSTM and 99.43% for CNN.

PDMLP: Phishing Detection using Multilayer Perceptron

A new method to develop a phishing detection system based on a multilayer perceptron (PDMLP), which used on two types of datasets and showed that PDMLP provides better performance in comparison to KNN, SVM, C4.5 Decision Tree, RF, and RoF to classifiers.