A Comparative Study of Two Different Spam Detection Methods

2019 ◽  
pp. 95-105
Author(s):  
Haoyu Wang ◽  
Bingze Dai ◽  
Dequan Yang
Keyword(s):  
Comparative Study ◽  
Detection Methods ◽  
Spam Detection

scholarly journals Comparative Study of Web Spam Detection using Data Mining

2013 ◽  
Vol 68 (18) ◽  
pp. 26-29
Author(s):  
Chirag Nathwani ◽  
Viralkumar Prajapati ◽  
Deven Agravat
Keyword(s):  
Data Mining ◽  
Comparative Study ◽  
Spam Detection ◽  
Web Spam ◽  
Using Data

scholarly journals A Comparative Study of Network Securities on Mobile Transaction

2021 ◽  
Author(s):  
Geoffrey Tyolaha ◽  
Moses Israel
Keyword(s):  
Wireless Networks ◽  
Intrusion Detection ◽  
Human Factors ◽  
Comparative Study ◽  
Mobile Networks ◽  
Detection Methods ◽  
Mobile Payments ◽  
4G Networks ◽  
Mobile Transaction ◽  
Average User

In recent years, the number of mobile transactions has skyrocketed. Because mobile payments are made on the fly, many consumers prefer the method to the traditional local payment approach. The rise in mobile payments has inspired this study into the security of mobile networks in order to instill trust in those who may be involved in the transaction in some way. This report is a precursor to explain and compare some of the most popular wireless networks that enable mobile payments, from a security standpoint, this research presents, explains, and compares some of the most common wireless networks that enable mobile payments. Threat models in 3G with connections to GSM, WLAN, and 4G networks are classified into four categories: attacks on privacy, attacks on integrity, attacks on availability, and assaults on authentication. In addition, we offer classification countermeasures which are divided into three categories: cryptographic methods, human factors, and intrusion detection methods. One of the most important aspects we analyze is the security procedures that each network employs. Since the security of these networks is paramount, it gives hope to subscribers. In summary, the study aims to verify if mobile payments offer acceptable security to the average user.

scholarly journals Effective Opinion Spam Detection: A Study on Review Metadata Versus Content

2020 ◽  
Vol 5 (2) ◽  
pp. 76-110
Author(s):  
Ajay Rastogi ◽  
Monica Mehrotra ◽  
Syed Shafat Ali
Keyword(s):  
Computation Time ◽  
Detection Methods ◽  
Spam Detection ◽  
Time Analysis ◽  
Hybrid Features ◽  
Versus Content ◽  
Behavioral Features ◽  
Opinion Spam ◽  
Real World Datasets ◽  
Practical Implications

AbstractPurposeThis paper aims to analyze the effectiveness of two major types of features—metadata-based (behavioral) and content-based (textual)—in opinion spam detection.Design/methodology/approachBased on spam-detection perspectives, our approach works in three settings: review-centric (spam detection), reviewer-centric (spammer detection) and product-centric (spam-targeted product detection). Besides this, to negate any kind of classifier-bias, we employ four classifiers to get a better and unbiased reflection of the obtained results. In addition, we have proposed a new set of features which are compared against some well-known related works. The experiments performed on two real-world datasets show the effectiveness of different features in opinion spam detection.FindingsOur findings indicate that behavioral features are more efficient as well as effective than the textual to detect opinion spam across all three settings. In addition, models trained on hybrid features produce results quite similar to those trained on behavioral features than on the textual, further establishing the superiority of behavioral features as dominating indicators of opinion spam. The features used in this work provide improvement over existing features utilized in other related works. Furthermore, the computation time analysis for feature extraction phase shows the better cost efficiency of behavioral features over the textual.Research limitationsThe analyses conducted in this paper are solely limited to two well-known datasets, viz., YelpZip and YelpNYC of Yelp.com.Practical implicationsThe results obtained in this paper can be used to improve the detection of opinion spam, wherein the researchers may work on improving and developing feature engineering and selection techniques focused more on metadata information.Originality/valueTo the best of our knowledge, this study is the first of its kind which considers three perspectives (review, reviewer and product-centric) and four classifiers to analyze the effectiveness of opinion spam detection using two major types of features. This study also introduces some novel features, which help to improve the performance of opinion spam detection methods.

scholarly journals A Study on Spam Detection Methods for Safe SMS Communication

2018 ◽  
Vol 7 (3.12) ◽  
pp. 790 ◽  
Author(s):  
Shailee Bhatia ◽  
. .
Keyword(s):  
Electronic Communication ◽  
Security System ◽  
Detection Methods ◽  
Spam Detection

The electronic communication enables the instant and all type availability of user. The different form of information transition can be drawn in the form of SMS and emails. But these emails and SMS systems are also used by the individuals and firm as medium of their advertisement. Spam messages not only involves the unwanted messages but it also includes some viruses and threat to the security system. In this paper, a study to the SMS filtration methods is provided. The paper has explored the types of SMS spams, its threats and various filtration methods to detect the spam SMS.  

scholarly journals Spam Review Detection Techniques: A Systematic Literature Review

2019 ◽  
Vol 9 (5) ◽  
pp. 987 ◽  
Author(s):  
Naveed Hussain ◽  
Hamid Turab Mirza ◽  
Ghulam Rasool ◽  
Ibrar Hussain ◽  
Mohammad Kaleem
Keyword(s):  
Feature Extraction ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Online Reviews ◽  
Detection Methods ◽  
Spam Detection ◽  
Successful Implementation ◽  
Comprehensive Review ◽  
Detection Techniques ◽  
Review Spam

Online reviews about the purchase of products or services provided have become the main source of users’ opinions. In order to gain profit or fame, usually spam reviews are written to promote or demote a few target products or services. This practice is known as review spamming. In the past few years, a variety of methods have been suggested in order to solve the issue of spam reviews. In this study, the researchers carry out a comprehensive review of existing studies on spam review detection using the Systematic Literature Review (SLR) approach. Overall, 76 existing studies are reviewed and analyzed. The researchers evaluated the studies based on how features are extracted from review datasets and different methods and techniques that are employed to solve the review spam detection problem. Moreover, this study analyzes different metrics that are used for the evaluation of the review spam detection methods. This literature review identified two major feature extraction techniques and two different approaches to review spam detection. In addition, this study has identified different performance metrics that are commonly used to evaluate the accuracy of the review spam detection models. Lastly, this work presents an overall discussion about different feature extraction approaches from review datasets, the proposed taxonomy of spam review detection approaches, evaluation measures, and publicly available review datasets. Research gaps and future directions in the domain of spam review detection are also presented. This research identified that success factors of any review spam detection method have interdependencies. The feature’s extraction depends upon the review dataset, and the accuracy of review spam detection methods is dependent upon the selection of the feature engineering approach. Therefore, for the successful implementation of the spam review detection model and to achieve better accuracy, these factors are required to be considered in accordance with each other. To the best of the researchers’ knowledge, this is the first comprehensive review of existing studies in the domain of spam review detection using SLR process.

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