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 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.

Enhanced Bio-Inspired Algorithms for Detecting and Filtering Spam

2018 ◽  
pp. 179-215
Author(s):  
Hadj Ahmed Bouarara
Keyword(s):  
Electronic Communication ◽  
Distance Measure ◽  
Machine Learning Algorithms ◽  
Spam Detection ◽  
Social Bees ◽  
Representation Technique ◽  
Sensitive Parameters ◽  
Measure Entropy ◽  
Email Spam ◽  
F Measure

The internet era promotes electronic commerce and facilitates access to many services. In today's digital society, the explosion in communication has revolutionized the field of electronic communication. Unfortunately, this technology has become incontestably the original source of malicious activities, especially the plague called undesirables email (SPAM) that has grown tremendously in the last few years. This chapter unveils fresh bio-inspired techniques (artificial social cockroaches [ASC], artificial haemostasis system [AHS], and artificial heart lungs system [AHLS]) and their application for SPAM detection. For the experimentation, the authors used the benchmark SMS Spam corpus V.0.1 and the validation measures (recall, precision, f-measure, entropy, accuracy, and error). They optimize the sensitive parameters of each algorithm (text representation technique, distance measure, weightings, and threshold). The results are positive compared to the result of artificial social bees and machine-learning algorithms (decision tree C4.5 and K-means).

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.

New Bio Inspired Techniques in the Filtering of Spam

2020 ◽  
pp. 693-726
Author(s):  
Hadj Ahmed Bouarara ◽  
Reda Mohamed Hamou ◽  
Abdelmalek Amine
Keyword(s):  
Electronic Communication ◽  
Distance Measure ◽  
Machine Learning Algorithms ◽  
Spam Detection ◽  
Social Bees ◽  
Representation Technique ◽  
Sensitive Parameters ◽  
Measure Entropy ◽  
Email Spam ◽  
F Measure

The internet era promotes electronic commerce and facilitates access to many services. In today's digital society the explosion in communication has revolutionized the field of electronic communication. Unfortunately, this technology has become incontestably the original source of malicious activities, especially the plague called undesirables email (SPAM) that has grown tremendously in the last few years. This paper deals on the unveiling of fresh bio-inspired techniques (artificial social cockroaches (ASC), artificial haemostasis system (AHS) and artificial heart lungs system (AHLS)) and their application for SPAM detection. For the authors' experimentation, they have used the benchmark SMS Spam corpus V.0.1 and the validation measures (recall, precision, f-measure, entropy, accuracy and error). They have optimising the sensitive parameters of each algorithm (text representation technique, distance measure, weightings, and threshold). The results are positive compared to the result of artificial social bees and machine learning algorithms (decision tree C4.5 and K-means).

LSSVM-Based Social Spam Detection Model

2013 ◽  
Vol 765-767 ◽  
pp. 1281-1286
Author(s):  
Xiao Lei Yang ◽  
Yi Dan Su ◽  
Jin Ping Mo
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Linear Equations ◽  
Detection Methods ◽  
Support Vector ◽  
Spam Detection ◽  
Squared Error Loss Function ◽  
Detection Model ◽  
Squared Error ◽  
Empirical Function

To Resolve the garbage tag issue in Folksonomy, Lssvm algorithm for social spam detection model (least Squares support vector machine classifiers) was proposed. The method of inequality change the constraints in the traditional support vector machine into equality constraints, and take the empirical function of the squared error loss function as the Experience function in training set. so that the quadratic programming problem convert QP into solving linear equations, it was improving solution the speed of solution and accuracy of convergence.The experimental results show that we have got higher classification accuracyand less predict time than traditional svm detection methods based on least squares support vector machine algorithm garbage tag detection model.

Spam detection using semantic web in mail services

2018 ◽  
Vol 7 (2.7) ◽  
pp. 44
Author(s):  
S Bhavika ◽  
B Prema Sindhuri ◽  
G Bhavana
Keyword(s):  
Semantic Web ◽  
Detection Method ◽  
The Other ◽  
Detection Methods ◽  
Spam Detection ◽  
Daily Lives ◽  
Do So

Electronic mails have become a part of our daily lives to exchange different type of information and messages. They provide a great medium to communicate with large number of people in a single stretch. This made so many marketing groups to think that email is a great platform for publicizing their goods or products. Not only are these marketers there so many other types of users who wants to make use of these emails for their own needs. As the time prolongs, this had become a problem for the other users because of the continuous undesired electronic messages sent by different marketing and some other unauthorized users. These messages are termed as spam messages. These spam mails have become a serious issue and there is a need to clear away all these junk mails. To do so different spam detection methodologies are developed and employed for providing an effective mailing service to the users. In this paper, we present various spam detection methods that are existing and also finding the accurate, effective and reliable spam detection method.

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