Approach For Spam Detection Research Articles

Spam Detection on Summarized Graph

As the popularity of social networking sites continues to increase, spam accounts are also on the rise. Over the past few years, social networking and information-sharing microblogging websites such as Twitter and Sina Weibo have gained popularity. Unsolicited content, such as social spam, has also been exploited by spammers to overwhelm most users unfairly. In contrast to existing work, this paper uses a novel graph-based approach for spam detection. The problem of graph summarization has practical applications involving visualization and graph compression. As graph-structured databases become popular and prominent, summarizing and compressing graph-structured databases can become more and more valuable. Our experimental results demonstrate the usefulness and efficiency of our proposed strategy. The accuracy of the graph is considered before and after Graph Summarization using MultiNominal NB and then compared with other machine learning algorithms. Various algorithms are considered, and it is found that MultiNominal NB gives the lowest training time and the highest accuracy. The training time of MultiNominal NB is found to be 0.55 sec before graph summarization. After graph summarization, the training time is optimized to be 0.02 seconds, and the accuracy value is 96.64%.

E-mail Spam Classification Using Grasshopper Optimization Algorithm and Neural Networks

Spam has turned into a big predicament these days, due to the increase in the number of spam emails, as the recipient regularly receives piles of emails. Not only is spam wasting users’ time and bandwidth. In addition, it limits the storage space of the email box as well as the disk space. Thus, spam detection is a challenge for individuals and organizations alike. To advance spam email detection, this work proposes a new spam detection approach, using the grasshopper optimization algorithm (GOA) in training a multilayer perceptron (MLP) classifier for categorizing emails as ham and spam. Hence, MLP and GOA produce an artificial neural network (ANN) model, referred to (GOAMLP). Two corpora are applied Spam Base and UK-2011 Web spam for this approach. Finally, the finding represents evidence that the proposed spam detection approach has achieved a better level in spam detection than the status of the art.

A novel approach for spam detection based on association rule mining and genetic algorithm

Spam detection is considered to be one of the most challenging issues in Online Social Networks (OSNs). In this paper, a supervised method is used to detect spam on these platforms. The accuracy of supervised methods depended on two factors: (i) the desired feature selection and (ii) the use of an appropriate classifier. An innovative method is also used for the first factor. This method is a combination of association rule mining and genetic algorithm with the aim of the desired feature selection from a variety of features. On the other hand, the second factor uses a large number of popular classifiers. The proposed method is assessed on three datasets, and the results show the effectiveness of the proposed feature selection method on the accuracy of the classifiers. The average accuracy for both approaches compared to the basic methods is 87.99% and 95.24%, respectively.

A feature selection approach for spam detection in social networks using gravitational force-based heuristic algorithm

A feature selection approach for spam detection in social networks using gravitational force-based heuristic algorithm

An Overview of Machine Learning Techniques for Spam Detection

Spam is nothing but irrelevant content with low quality information sent over the internet typically to a large number of users for the purpose of phishing, spreading malware. Spam is commonly found as images, texts or videos, advertisements & on social networking sites. Different approaches to tackle these unwanted messages, including challenge response model, white listing, blacklisting, are in place to deal with this issue. These solutions are available for end users but due to the dynamic nature of the Web, there are no 100% secure systems around the world which can handle this problem. Machine learning provides better mechanisms that are able to control spam. This paper aims to analyze existing research works in spam detection strategies and approaches, state of art, the phenomenon of spam detection, to explore the rudiment of spam detection, to proposed detection schemes and potential online mitigation schemes. The paper will survey various anti-spam strategies. In the literature we have studied that many anti-spam strategies have been discovered and worked on but they are still open challenges to these different approaches and techniques, while some of them are highlighted in this article. It is very important to work on spam detection and reposition it for the better of the world.

A multi-swarm optimisation approach for spam detection in online social networks

A multi-swarm optimisation approach for spam detection in online social networks

A multi-swarm optimisation approach for spam detection in online social networks

Online Social Networks (OSNs) play a crucial role in communication systems for rapid message broadcasting and information sharing. Facebook is one of the popular OSNs that has the highest number of active users as per the statistical reports. However, it witnesses challenges due to the presence of spammers, whose actions may make the environment unfavourable for users. The spammers hold social accounts that are particularly created for personal benefit. As the number of users on Facebook increases, the number of illegitimate accounts proportionally rises, which leads to distress in the OSN environment. Several methods have been proposed in the literature to address the spam profile detection problem; however, they become obsolete as the spammers evolve. Hence, in this paper, a novel Multi-Swarm-Whale Optimisation Algorithm (MS-WOA) is proposed for feature selection to detect spam profiles on Facebook. Further IP-address-based features tailored for Facebook are also proposed. The performance of the proposed MS-WOA is compared with the recently developed methods and outperforms them in terms of accuracy and robustness.

A Spam Transformer Model for SMS Spam Detection

In this paper, we aim to explore the possibility of the Transformer model in detecting the spam Short Message Service (SMS) messages by proposing a modified Transformer model that is designed for detecting SMS spam messages. The evaluation of our proposed spam Transformer is performed on SMS Spam Collection v.1 dataset and UtkMl’s Twitter Spam Detection Competition dataset, with the benchmark of multiple established machine learning classifiers and state-of-the-art SMS spam detection approaches. In comparison to all other candidates, our experiments on SMS spam detection show that the proposed modified spam Transformer has the optimal results on the accuracy, recall, and F1-Score with the values of 98.92%, 0.9451, and 0.9613, respectively. Besides, the proposed model also achieves good performance on the UtkMl’s Twitter dataset, which indicates a promising possibility of adapting the model to other similar problems.

Analyzing the effectiveness of semi-supervised learning approaches for opinion spam classification

Opinion spam detection is concerned with identifying fake reviews that are deliberately placed to either promote or discredit a product. Opinionated social media like product reviews are increasingly important resources for people as well as businesses in the decision-making process and can be easily manipulated by opportunistic individuals. To reduce this increasing impact of opinion spams, opinion spam detection approaches have been proposed, which adopt mostly supervised classification methods. However, in practice, the provided data is largely not labeled and therefore semi-supervised learning approaches are required instead. To this end, this study aims to analyze the effectiveness of several semi-supervised learning approaches for opinion spam classification. Four different semi-supervised methods are evaluated on a dataset of both genuine and deceptive hotel reviews. The results are compared with several traditional classification methods using the same amount of labeled data. According to this study, the self-training algorithm with Naive Bayes as the base classifier yields 93% accuracy. Results show that self-training is the only approach, out of the four tested semi-supervised models, that outperforms traditional supervised classification models when limited data is available. This study further shows that self-training can mitigate labeling efforts while retaining high model performance, which is useful for scenarios where limited data is available or retrieving labeled data is more costly.

Identifying spammers and fake users identification in online social networking sites

Online social networking sites are new platforms for spreading spammers and fake news for attackers. Recently, the detection of spammers and identification of fake users on Twitter has become a common area of research in contemporary online social Networks (OSNs). In this paper, we perform a review of techniques used for detecting spammers on Twitter. Twitter spam detection approaches is presented that classifies the techniques based on their ability to detect: (i) fake content, (ii) spam based on URL, (iii) spam in trending topics, and (iv) fake users. The presented techniques are also compared based on various features, such as user features, content features, graph features, structure features, and time features.

An ensemble approach for spam detection in Arabic opinion texts

Nowadays, individuals express experiences and opinions through online reviews. These has an influence on online marketing and obtaining real knowledge about products and services. However, some of the online reviews can be unreal. They may have been written to promote low-quality products/services or sabotage a product/service reputation to mislead potential customers. Such misleading reviews are known as spam reviews and require crucial attention. Prior spam detection research focused on English reviews, with less attention to other languages. The detection of spam reviews in Arabic online sources is a relatively new topic despite the relatively huge amount of data generated. Therefore, this paper contributes to such topic by presenting four different Arabic spam reviews detection methods, while putting more focus towards the construction and evaluation of an ensemble approach. The proposed ensemble method is based on integrating a rule-based classifier with machine learning techniques, while utilizing content-based features that depend on N-gram features and Negation handling. The four proposed methods are evaluated on two datasets of different sizes. The results indicate the efficiency of the ensemble approach where it achieves a classification accuracy of 95.25% and 99.98% for the two experimented datasets and outperforming existing related work by far of 25%.

Spam e-mail classification for the Internet of Things environment using semantic similarity approach

Unauthorized service or product advertising messages sent via electronic mails are called as spam e-mails. Detecting spam e-mail remains a challenging task. Existing countermeasures based on the statistical keyword, conceptual and IP address-based blacklists are not efficient due to difficulty in finding new attack patterns generated by the Internet of Things botnet devices. The other spam detection approaches rely on a hybrid of conceptual knowledge engineering with machine learning techniques. But, modern spammers evade the hybrid techniques through word polysemy and word ambiguity due to the context-sensitive nature of words. In this paper, the integration of Naive Bayesian classification with conceptual and semantic similarity technique is proposed to combat the ambiguity raised through polysemy in spam detection. To analyse the effectiveness of our approach, the experiments were conducted on benchmark data sets such as Spambase, PU1, Enron corpus, and Ling-spam. From the experimental results, it is evident that our proposed system achieves high accuracy of 98.89% than the existing approaches.

Spammer Detection and Fake User Identification on Social Networks

Social networking sites engage millions of users around the world. The users' interactions with these social sites, such as Twitter and Facebook have a tremendous impact and occasionally undesirable repercussions for daily life. The prominent social networking sites have turned into a target platform for the spammers to disperse a huge amount of irrelevant and deleterious information. Twitter, for example, has become one of the most extravagantly used platforms of all times and therefore allows an unreasonable amount of spam. Fake users send undesired tweets to users to promote services or websites that not only affect legitimate users but also disrupt resource consumption. Moreover, the possibility of expanding invalid information to users through fake identities has increased that results in the unrolling of harmful content. Recently, the detection of spammers and identification of fake users on Twitter has become a common area of research in contemporary online social Networks (OSNs). In this paper, we perform a review of techniques used for detecting spammers on Twitter. Moreover, a taxonomy of the Twitter spam detection approaches is presented that classifies the techniques based on their ability to detect: (i) fake content, (ii) spam based on URL, (iii) spam in trending topics, and (iv) fake users. The presented techniques are also compared based on various features, such as user features, content features, graph features, structure features, and time features. We are hopeful that the presented study will be a useful resource for researchers to find the highlights of recent developments in Twitter spam detection on a single platform.

A Neural Network-Based Ensemble Approach for Spam Detection in Twitter

As the social networking sites get more popular, spammers target these sites to spread spam posts. Twitter is one of the most popular online social networking sites where users communicate and interact on various topics. Most of the current spam filtering methods in Twitter focus on detecting the spammers and blocking them. However, spammers can create a new account and start posting new spam tweets again. So there is a need for robust spam detection techniques to detect the spam at tweet level. These types of techniques can prevent the spam in real time. To detect the spam at tweet level, often features are defined, and appropriate machine learning algorithms are applied in the literature. Recently, deep learning methods are showing fruitful results on several natural language processing tasks. We want to use the potential benefits of these two types of methods for our problem. Toward this, we propose an ensemble approach for spam detection at tweet level. We develop various deep learning models based on convolutional neural networks (CNNs). Five CNNs and one feature-based model are used in the ensemble. Each CNN uses different word embeddings (Glove, Word2vec) to train the model. The feature-based model uses content-based, user-based, and n-gram features. Our approach combines both deep learning and traditional feature-based models using a multilayer neural network which acts as a meta-classifier. We evaluate our method on two data sets, one data set is balanced, and another one is imbalanced. The experimental results show that our proposed method outperforms the existing methods.

IPV4 and IPV6 based hybrid approach for spam and virus detection

The proposed email setup consist of multiple mail servers distributed at two levels to achieve desirable email access performance. It is a well-established fact that 99% of the emails received over internet are either spam or contained viruses and such emails can be dropped at the first entry point of the Network. Thus, the first level in the proposed architecture has been taken as an email gateway which is equipped with antivirus and anti-spam software. Spam assassion is an open source freeware software for filtering of the spam emails. Perl based spamassassion id to CPU and Memory hungry for heavily loaded server thus this new arrangement would then overcome the Problem of slow Email access for users by detaching spamassassion from email repository servers. The second level of the servers with email repositories for users all the email servers used in this implementation would be Linux X86 servers. Virtualization technique presents a software interface to virtual machines that is similar but not identical to that of the underlying hardware. First level is implemented in Virtual machine. So as to provide scalability, portability, migration and vendor independent.

Online Social Networking services and Spam Detection Approaches in Opinion Mining- A review

Social network platforms have gained enormous growth in present days. Users are more attracted towards social network sites such as Twitter, Facebook, micro blogs and YouTube to express their opinion and views and share the knowledge. With the tremendous increase in technological growth and with the diversity of products, online e-commerce servicing sites are becoming competitor's for each other to increase their proliferation in business offering to post positive product spam reviews. Users generally express their opinion based on different sentiment orientations, ratings and the features of the product. This tends to create ambiguity at the customers end in arriving at a decision based on the criticism, building fake opinion on the products. Opinion spam detection provides scope in building opinion from the reviews of various networking sites. A novel framework using meta-heuristic and k-means clustering approach is proposed for identifying opinion spam detection using flower pollination, grey wolf and moth flame. Amazon automotive product dataset is chosen for analysis and it is observed that grey wolf algorithm performs better than flower pollination and moth flame optimisation algorithms in terms of improved convergence speed, mean, standard deviation, variance and elapsed run time.

Online social networking services and spam detection approaches in opinion mining - a review

Online social networking services and spam detection approaches in opinion mining - a review

Few are as Good as Many: An Ontology-Based Tweet Spam Detection Approach

Due to the high popularity of Twitter, spammers tend to favor its use in spreading their commercial messages. In the context of detecting twitter spams, different statistical and behavioral analysis approaches were proposed. However, these techniques suffer from many limitations due to: 1) ongoing changes to Twitter’s streaming API which constrains access to a user’s list of followers/followees; 2) spammer’s creativity in building diverse messages; 3) use of embedded links and new accounts; and 4) need for analyzing different characteristics about users without their consent. To address the aforementioned challenges, we propose a novel ontology-based approach for spam detection over Twitter during events by analyzing the relationship between ham user tweets versus spams. Our approach relies solely on public tweet messages while performing the analysis and classification tasks. In this context, ontologies are derived and used to generate a dictionary that validates real tweet messages from random topics. Similarity ratio among the dictionary and tweets is used to reflect the legitimacy of the messages. Experiments conducted on real tweet data illustrate that message-to-message techniques achieved a low detection rate compared with our ontology-based approach which outperforms them by approximately 200%, in addition to promising scalability for large data analysis.

Review Spam Detection by Highlighting Potential Spammers and Diminishing Their Effect

Nowadays, millions of products and services are available to the public online. Therefore, searching for the best products which meets individuals' expectations would be difficult due to the existence of too many alternative choices. One of the most reliable approaches to choose a product or service is to exploit the experience of people who have already tried them, and are expected to have reported their almost honest opinions about them. A reviewing system is a place where individuals share their experience on products and services. Individuals may read and/or write their reviews which may be neutral and professional or biased. Moreover, companies utilize reviewing systems to apply opinion mining techniques in order to improve their goods or services and may be to watch their competitors. However, the popularity of reviewing systems ignites this motivation for some people to try to influence viewers by entering their fake reviews to promote some products or defame some others. These spam reviews should be detected and eliminated to prevent misleading potential customers and unethically affect the market. Opinion mining should be adapted to locate and eliminate potential spam reviews. In this paper, some review spam detection approaches have been proposed and examined over a sample dataset. The proposed approaches consider patterns that existed in trends of reviews, as well as reviewers' behavior. The approaches depend on various strategies such as observing abnormal trends, detecting uncommon or suspicious behaviors, investigating group activities, among others. The reported test results revealed some promising outcome.

Spam Detection Approaches and Strategies: A Phenomenons

Spam Detection Approaches and Strategies: A Phenomenons