Phishing Detection on Ethereum via Learning Representation of Transaction Subgraphs

2020 ◽  
pp. 178-191
Author(s):  
Zihao Yuan ◽  
Qi Yuan ◽  
Jiajing Wu
Keyword(s):  
Phishing Detection

URL Based Analysis for Phishing Detection Using Data Mining

2018 ◽  
Author(s):  
Nikita Gawade ◽  
Sayali Mundekar ◽  
Nilam Vare ◽  
Ruchi Gada ◽  
Smita Bansod
Keyword(s):  
Data Mining ◽  
Using Data ◽  
Phishing Detection

Temporal Resilience of Phishing Detection Models in Machine Learning

2019 ◽  
Author(s):  
Arvind Abraham ◽  
Gilad Gressel ◽  
Krishnashree Achuthan
Keyword(s):  
Machine Learning ◽  
Phishing Detection

Survey and Analysis on AI Based Phishing Detection Techniques

2021 ◽  
Author(s):  
Nithin Valiyaveedu ◽  
Sangeetha Jamal ◽  
Roshan Reju ◽  
Vysakh Murali ◽  
Nithin K M
Keyword(s):  
Detection Techniques ◽  
Phishing Detection

scholarly journals Artificial Intelligence Techniques for Phishing Detection

2019 ◽  
Vol 8 (11) ◽  
pp. 2330-2335
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Neural Systems ◽  
Element Determination ◽  
Artificial Intelligence Techniques ◽  
The Neural Network ◽  
Proper Number ◽  
Phishing Detection

The objective of this undertaking is to apply neural systems to phishing email recognition and assess the adequacy of this methodology. We structure the list of capabilities, process the phishing dataset, and execute the Neural Network frameworks. we analyze its exhibition against that of other real Artificial Intelligence Techniques – DT , K-nearest , NB and SVM machine.. The equivalent dataset and list of capabilities are utilized in the correlation. From the factual examination, we infer that Neural Networks with a proper number of concealed units can accomplish acceptable precision notwithstanding when the preparation models are rare. Additionally, our element determination is compelling in catching the qualities of phishing messages, as most AI calculations can yield sensible outcomes with it.

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.

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