scholarly journals A Multi-Layer Architecture for Spam-Detection System

2014 ◽  
Author(s):  
Vivek Shandilya ◽  
Fahad Polash ◽  
Sajjan Shiva
Keyword(s):  
Detection System ◽  
Spam Detection

Spam Detection System Using Supervised ML

2021 ◽  
Author(s):  
Abhila B ◽  
Delphin Periyanayagi M ◽  
Koushika M ◽  
Mabel Nirmala Joseph ◽  
Dhanalakshmi R
Keyword(s):  
Detection System ◽  
Spam Detection

scholarly journals Editorial

2021 ◽  
Vol 27 (4) ◽  
pp. 323-323
Author(s):  
Christian Gütl
Keyword(s):  
Financial Support ◽  
Detection System ◽  
Heterogeneous Data ◽  
Spam Detection ◽  
Regular Expressions ◽  
Challenging Problem ◽  
Crucial Issue ◽  
High Quality ◽  
Distributed Information ◽  
Great Support

I am pleased to announce the fourth issue of 2021. As always, I would like to express my sincere appreciation for the great support that makes the continued publication of novel and high quality articles possible. Thus, I would like to thank all authors for their sound research contributions, the reviewers for their very helpful suggestions and the consortium members for their financial support. I would also like to report on further achievements regarding our new platform. We have successfully migrated all the information of the Board of Editors and we have also started to use the new review module. Due to the cooperation with Pensoft Inc., our new platform provider, we will also be able to offer review acknowledgment on the Publons portal in the future. In this regular issue, I am very pleased to introduce four accepted papers from three different countries and 14 involved authors. Martin Berglund, Brink van der Merwe, and Steyn van Litsenborgh from South Africa investigate in their article regular expressions which contain lookaheads in addition to the standard operators of union, concatenation, and Kleene star. Fairouz Fakhfakh, Slim Kallel and Saoussen Cheikhrouhou from Tunisia research and discuss in their work a crucial issue in modern distributed information systems, i.e. how to verify the correctness of Cloud and Fog systems based on formal verification. Marcia Henke, Eulanda Santos, Eduardo Souto, and Altair O. Santin from Brazil introduce their enhanced spam detection system which is based on analyzing the evolution of features. And finally, also from Brazil, Marcelo Aires Vieira, Elivaldo Lozer Fracalossi Ribeiro, Daniela Barreiro Claro, and Babacar Mane investigate the challenging problem of integrating heterogeneous DaaS and DBaaS sources and explore the Data Join (DJ) method for integrating heterogeneous data.

scholarly journals Spam Detection Framework using ML Algorithm

2020 ◽  
Vol 8 (6) ◽  
pp. 5326-5329
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Networking ◽  
Information Sharing ◽  
Detection System ◽  
Reliable Data ◽  
Machine Learning Algorithms ◽  
Spam Detection ◽  
Function Type ◽  
Use Of Social Media

The current use of social media has created incomparable amounts of social data, as it is a cheap and popular information sharing communication platform. Nowadays, a huge percentage of people depend on the accessible material on social networking in their choices (e.g. comments and suggestions about a subject or product). This feature on exchanging knowledge with a wide number of users has quickly prompted social spammers to exploit the network of confidence to distribute spam messages and support personal forums, advertising, phishing, scams and so on. Identifying these spammers and spam material is a hot subject of study, and while large amounts of experiments have recently been conducted to this end, so far the methodologies are only barely able to identify spam feedback, and none of them demonstrates the value of each derived function type. In this study, we have suggested a machine learning-based spam detection system that determines whether or not a specific message in the dataset is spam using a set of machine learning algorithms. Four main features have been used; including user-behavioral, user-linguistic, reviewbehavioral and review-linguistic, to improve the spam detection process and to gather reliable data

Feature Subset Selection Using Binary Quantum Particle Swarm Optimization for Spam Detection System

2014 ◽  
Vol 20 (1) ◽  
pp. 188-192 ◽  
Author(s):  
Amir Rajabi Behjat ◽  
Aida Mustapha ◽  
Hossein Nezamabadi-Pour ◽  
Md. Nasir Sulaiman ◽  
Norwati Mustapha
Keyword(s):  
Particle Swarm Optimization ◽  
Quantum Particle ◽  
Detection System ◽  
Particle Swarm ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Spam Detection ◽  
Feature Subset ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

A data mining based spam detection system for YouTube

2013 ◽  
Author(s):  
Rashid Chowdury ◽  
Md. Nuruddin Monsur Adnan ◽  
G. A. N. Mahmud ◽  
Rashedur M Rahman
Keyword(s):  
Data Mining ◽  
Detection System ◽  
Spam Detection

scholarly journals Spam Detection Based on Feature Evolution to Deal with Concept Drift

2021 ◽  
Vol 27 (4) ◽  
pp. 364-386
Author(s):  
Marcia Henke ◽  
Eulanda Santos ◽  
Eduardo Souto ◽  
Altair O Santin
Keyword(s):  
Knowledge Transfer ◽  
Detection System ◽  
Concept Drift ◽  
Low Cost ◽  
Training Data ◽  
Classification Model ◽  
Easy Access ◽  
Spam Detection ◽  
Concept Drift Detection ◽  
E Mail

Electronic messages are still considered the most significant tools in business and personal applications due to their low cost and easy access. However, e-mails have become a major problem owing to the high amount of junk mail, named spam, which fill the e-mail boxes of users. Several approaches have been proposed to detect spam, such as filters implemented in e-mail servers and user-based spam message classification mechanisms. A major problem with these approaches is spam detection in the presence of concept drift, especially as a result of changes in features over time. To overcome this problem, this work proposes a new spam detection system based on analyzing the evolution of features. The proposed method is divided into three steps: 1) spam classification model training; 2) concept drift detection; and 3) knowledge transfer learning. The first step generates classification models, as commonly conducted in machine learning. The second step introduces a new strategy to avoid concept drift: SFS (Similarity-based Features Se- lection) that analyzes the evolution of the features taking into account similarity obtained between the feature vectors extracted from training data and test data. Finally, the third step focuses on the following questions: what, how, and when to transfer acquired knowledge? The proposed method is evaluated using two public datasets. The results of the experiments show that it is possible to infer a threshold to detect changes (drift) in order to ensure that the spam classification model is updated through knowledge transfer. Moreover, our anomaly detection system is able to perform spam classification and concept drift detection as two parallel and independent tasks.

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