Stop-Phish: an intelligent phishing detection method using feature selection ensemble

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
A. V. Ramana ◽  
K. Lakshmana Rao ◽  
Routhu Srinivasa Rao
Keyword(s):  
Feature Selection ◽  
Detection Method ◽  
Phishing Detection

scholarly journals A Survey on Phishing Detection and The Importance of Feature Selection In Data Mining Classification Algorithms

2020 ◽  
pp. 11-18
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Support Vector ◽  
Classification Algorithms ◽  
End User ◽  
Preparation Methods ◽  
Survey Paper ◽  
Vector Machines ◽  
Feature Selection Techniques ◽  
Phishing Detection

: In this era of Internet, the issue of security of information is at its peak. One of the main threats in this cyber world is phishing attacks which is an email or website fraud method that targets the genuine webpage or an email and hacks it without the consent of the end user. There are various techniques which help to classify whether the website or an email is legitimate or fake. The major contributors in the process of detection of these phishing frauds include the classification algorithms, feature selection techniques or dataset preparation methods and the feature extraction that plays an important role in detection as well as in prevention of these attacks. This Survey Paper studies the effect of all these contributors and the approaches that are applied in the study conducted on the recent papers. Some of the classification algorithms that are implemented includes Decision tree, Random Forest , Support Vector Machines, Logistic Regression , Lazy K Star, Naive Bayes and J48 etc.

scholarly journals An intelligent chatter detection method based on EEMD and feature selection with multi-channel vibration signals

Measurement ◽  
2018 ◽  
Vol 127 ◽  
pp. 356-365 ◽  
Author(s):  
Yun Chen ◽  
Huaizhong Li ◽  
Liang Hou ◽  
Jun Wang ◽  
Xiangjian Bu
Keyword(s):  
Feature Selection ◽  
Detection Method ◽  
Chatter Detection ◽  
Vibration Signals

scholarly journals Phishing Detection Based on Machine Learning and Feature Selection Methods

2019 ◽  
Vol 13 (12) ◽  
pp. 171 ◽  
Author(s):  
Mohammad Almseidin ◽  
AlMaha Abu Zuraiq ◽  
Mouhammd Al-kasassbeh ◽  
Nidal Alnidami
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Web Pages ◽  
Selection Methods ◽  
Random Forest Algorithm ◽  
Phishing Detection ◽  
Enormous Number

With increasing technology developments, the Internet has become everywhere and accessible by everyone. There are a considerable number of web-pages with different benefits. Despite this enormous number, not all of these sites are legitimate. There are so-called phishing sites that deceive users into serving their interests. This paper dealt with this problem using machine learning algorithms in addition to employing a novel dataset that related to phishing detection, which contains 5000 legitimate web-pages and 5000 phishing ones. In order to obtain the best results, various machine learning algorithms were tested. Then J48, Random forest, and Multilayer perceptron were chosen. Different feature selection tools were employed to the dataset in order to improve the efficiency of the models. The best result of the experiment achieved by utilizing 20 features out of 48 features and applying it to Random forest algorithm. The accuracy was 98.11%.

Metamorphic Malware Detection Using Minimal Opcode Statistical Patterns

2018 ◽  
pp. 337-359
Author(s):  
Mahmood Fazlali ◽  
Peyman Khodamoradi
Keyword(s):  
Feature Selection ◽  
Decision Tree ◽  
High Speed ◽  
Detection Method ◽  
Malware Detection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Input File ◽  
Feature Vectors ◽  
Metamorphic Malware

High-speed and accurate malware detection for metamorphic malware are two goals in antiviruses. To reach beyond this issue, this chapter presents a new malware detection method that can be summarized as follows: (1) Input file is disassembled and classified to obtain the minimal opcode pattern as feature vectors; (2) a forward feature selection method (i.e., maximum relevancy and minimum redundancy) is applied to remove the redundant as well as irrelevant features; and (3) the process ends by classification through using decision tree. The results indicate the proposed method can effectively detect metamorphic malware in terms of speed, efficiency, and accuracy.

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