scholarly journals Building Robust Phishing Detection System: an Empirical Analysis

2020 ◽  
Author(s):  
Jehyun Lee ◽  
Pingxiao Ye ◽  
Ruofan Liu ◽  
Dinil Mon Divakaran ◽  
Mun Choon Chan
Keyword(s):  
Empirical Analysis ◽  
Detection System ◽  
Phishing Detection

scholarly journals Towards a Multi-Layered Phishing Detection

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4540
Author(s):  
Kieran Rendall ◽  
Antonia Nisioti ◽  
Alexios Mylonas
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Detection System ◽  
Single Layer ◽  
Supervised Machine Learning ◽  
Data Driven ◽  
Feature Sets ◽  
Phishing Attacks ◽  
Production Environments ◽  
Phishing Detection

Phishing is one of the most common threats that users face while browsing the web. In the current threat landscape, a targeted phishing attack (i.e., spear phishing) often constitutes the first action of a threat actor during an intrusion campaign. To tackle this threat, many data-driven approaches have been proposed, which mostly rely on the use of supervised machine learning under a single-layer approach. However, such approaches are resource-demanding and, thus, their deployment in production environments is infeasible. Moreover, most previous works utilise a feature set that can be easily tampered with by adversaries. In this paper, we investigate the use of a multi-layered detection framework in which a potential phishing domain is classified multiple times by models using different feature sets. In our work, an additional classification takes place only when the initial one scores below a predefined confidence level, which is set by the system owner. We demonstrate our approach by implementing a two-layered detection system, which uses supervised machine learning to identify phishing attacks. We evaluate our system with a dataset consisting of active phishing attacks and find that its performance is comparable to the state of the art.

scholarly journals Intelligent phishing detection system for e-banking using fuzzy data mining

2010 ◽  
Vol 37 (12) ◽  
pp. 7913-7921 ◽  
Author(s):  
Maher Aburrous ◽  
M.A. Hossain ◽  
Keshav Dahal ◽  
Fadi Thabtah
Keyword(s):  
Data Mining ◽  
Detection System ◽  
Fuzzy Data ◽  
Phishing Detection

scholarly journals Detecting Phishing Website with Machine Learning

2019 ◽  
Vol 8 (3) ◽  
pp. 5626-5629
Keyword(s):  
Machine Learning ◽  
Detection Rate ◽  
Detection System ◽  
Attack Detection ◽  
Svm Classifier ◽  
Cyber Attack ◽  
Phishing Attacks ◽  
Two Parameters ◽  
Phishing Detection

Attacks are many types to disturb the network or any other websites. Phishing attacks (PA) are a type of attacks which attack the website and damage the website and may lose the data. Many types of research have been done to prevent the attacks. To overcome this, in this paper, the integrated phishing attack detection system which is adopted with SVM classifier is implemented to detect phishing websites. Phishing is the cyber attack that will destroy the website and may attack with the virus. There are two parameters that can detect the final phishing detection rate such as Identity, and security. Phishing attacks also occur in various banking and e-commerce websites. This paper deals with the UCL machine learning phishing dataset which consists of 32 attributes. The proposed algorithm implements on this dataset and shows the performance.

CBR-PDS: a case-based reasoning phishing detection system

2018 ◽  
Vol 10 (7) ◽  
pp. 2593-2606 ◽  
Author(s):  
Hassan Abutair ◽  
Abdelfettah Belghith ◽  
Saad AlAhmadi
Keyword(s):  
Detection System ◽  
Case Based Reasoning ◽  
Phishing Detection ◽  
Case Based
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