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Journal articles on the topic 'Spam detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 September 2023

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1

Kim, So Yeon, and Kyung-Ah Sohn. "Graph-Based Spam Image Detection for Mobile Phone Spam Image Filtering." International Journal of Software Innovation 3, no. 4 (October 2015): 72–86. http://dx.doi.org/10.4018/ijsi.2015100106.

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Spam images in mobile phones have increasingly appeared these days. As the spam filtering systems become more sophisticated, spams are being more intelligent. Although detection of email-spams has been quite successful, there have not been effective solutions for detecting mobile phone spams yet, especially, spam images. In addition to the expensive image processing time, insufficient spam image data in mobile phones makes it challenging to train a general model. To address this issue, the authors propose a graph-based approach that utilizes graph structure in abundant e-mail spam dataset. The authors employ different clustering algorithms to find a subset of e-mail spam images similar to phone spam images. Furthermore, the performance behavior with respect to different image descriptors of Pyramid Histogram of Visual Words (PHOW) and RGB histogram is extensively investigated. The authors' results highlight that the proposed idea is fairly meaningful in increasing training data size, thus effectively improving image spam detection performance.

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Hemalatha, M., Sriharsha Katta, R. Sai Santosh, and Priyanka Priyanka. "E-MAIL SPAM DETECTION." International Journal of Computer Science and Mobile Computing 11, no. 1 (January 30, 2022): 36–44. http://dx.doi.org/10.47760/ijcsmc.2022.v11i01.006.

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E-mail is the most important form of communication. Used for a wide range of people including individuals and organizations. But these people using this e-mail they find it difficult to use because of spam mail. These spam emails are also called unsolicited bulk mail or junk mail. Spam emails are available randomly sent messages to people by anonymous users. Sites are trying to steal yours personal, electronic and financial information. An increase in spam emails leads to crime of theft of sensitive information, reduced productivity. Spam detection is dirty. The line between spam and non-spam messages is blurred, and the condition changes over time. From various attempts to automate spam detection, machine learning has so far proven to be the most effective and popular method of email providers. While we still see spam emails, a quick look at the trash folder will show how many spam is removed from our inbox daily due to machine learning algorithms. It is estimated that 40% of emails are spam mail. These spam wastes time, storage the space and width of the communication band. There are a few ways to receive spam emails but spam senders make it difficult for you to send users from a random sender address or by adding special characters at the beginning or end of the email. There are several machine learning methods for filtering spam emails including Naïve Bayes classifier, Vector support equipment, Neural Networks, Close Neighbour, Rough Sets and Random Forests. In this project we use the Naïve Bayes classifier to identify spam mail. The vast majority of people depend on what is available email or messages sent by a stranger. Possibly anyone can leave an email or message provide gold the opportunity for spam senders to write a spam message about us different interests. Spam fills in the inbox with a number of funny things mails. Slow down our internet speed. Theft useful information such as our details on our contact list. Identifying these people who post spam and spam content can be a a hot topic for research and strenuous activities. Email Spam is functionality of mass mailings. From the cost of Spam is heavily censored by the recipient, it is a successful post proper advertising. Spam email is a form of commercial advertising economically viable because email can be costly effective sender method. With this proposed model some message may be declared spam or not use Bayes' theorem and Naive Bayes’ Classifier and IP addresses of sender is usually found.

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Liu, Xiaoxu, Haoye Lu, and Amiya Nayak. "A Spam Transformer Model for SMS Spam Detection." IEEE Access 9 (2021): 80253–63. http://dx.doi.org/10.1109/access.2021.3081479.

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4

Wang, Junzhang, Diwen Xue, and Karen Shi. "An Ensemble Framework for Spam Detection on Social Media Platforms." International Journal of Machine Learning and Computing 11, no. 1 (January 2021): 77–84. http://dx.doi.org/10.18178/ijmlc.2021.11.1.1017.

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As various review sites grow in popularity and begin to hold more sway in consumer preferences, spam detection has become a burgeoning field of research. While there have been various attempts to resolve the issue of spam on the open web, specifically as it relates to reviews, there does not yet exist an adaptive and robust framework out there today. We attempt to address this issue in a domain-specific manner, choosing to apply it to Yelp.com first. We believe that while certain processes do exist to filter out spam reviews for Yelp, we have a comprehensive framework that can be extended to other applications of spam detection as well. Furthermore, our framework exhibited a robust performance even when trained on small datasets, providing an approach for practitioners to conduct spam detection when the available data is inadequate. To the best of our knowledge, our framework uses the most number of extracted features and models in order to finely tune our results. In this paper, we will show how various sets of online review features add value to the final performance of our proposed framework, as well as how different machine learning models perform regarding detecting spam reviews.

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Kumar D, Mr Girish. "Spam Detection in Twitter." International Journal for Research in Applied Science and Engineering Technology 8, no. 7 (July 31, 2020): 783–87. http://dx.doi.org/10.22214/ijraset.2020.30337.

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6

Heron, Simon. "Technologies for spam detection." Network Security 2009, no. 1 (January 2009): 11–15. http://dx.doi.org/10.1016/s1353-4858(09)70007-8.

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7

Panwar, Manish, Jayesh Rajesh Jogi, Mahesh Vijay Mankar, Mohamed Alhassan, and Shreyas Kulkarni. "Detection of Spam Email." American Journal of Innovation in Science and Engineering 1, no. 1 (December 30, 2022): 18–21. http://dx.doi.org/10.54536/ajise.v1i1.996.

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Spam, often known as unsolicited email, has grown to be a major worry for every email user. Nowadays, it is quite challenging to filter spam emails since they are made, created, or written in such a unique way that anti-spam filters cannot recognize them. In order to predict or categorize emails as spam, this paper compares and reviews the performance metrics of a few categories of supervised machine learning techniques, including Svm (Support Vector Machine), Random Forest, Decision Tree, Cnn, (Convolutional Neural Network), Knn(K Nearest Neighbor), Mlp(Multi-Layer Perceptron), Adaboost (AdaptiveBoosting), and Nave Bayes algorithm. Thegoal of this study is to analyze the specificsor content of the emails, discover a limited dataset, and create a classification model that can predict or categorize whether spam is present in an email. Transformers’ Bidirectional Encoder Representations) has been optimized to perform the duty of separating spam emails from legitimate emails (Ham). To put the text’s context into perspective, Bert uses attention layers. Results are contrasted with a baseline Dnn (deep neural network) modelthat consists of two stacked Dense layers and a Bilstm (bidirectional Long Short-Term Memory) layer. Results are also contrasted with a group of traditional classifiers, including k- Nn (k-nearest neighbours) and Nb (Naive Bayes). The model is tested for robustness andpersistence using two open-source data sets, one of which is utilized to train the model.

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8

V, Shoba, Ravi Shankar, Ramya Shree, Dhanush H, and Manjunath L. "Spam Detection Using Machine Learning." International Research Journal of Computer Science 10, no. 05 (June 23, 2023): 130–34. http://dx.doi.org/10.26562/irjcs.2023.v1005.05.

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Deep learning has emerged as a powerful technique for spam detection due to its ability to automatically learn relevant features from raw data. This abstract presents an overview of deep learning approaches for spam detection and highlights their effectiveness in combating the ever-evolving landscape of spam. The challenges of spam detection, includes the use of sophisticated techniques by spammers to evade traditional rule-based filters. Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown promise in addressing these challenges by automatically extracting high-level features from spam messages. The Spam Detection Using Machine Learning in this paper employs the Deep Learning model Bi-GRU to detect the spam messages. The advantages of this approach, is that it has the ability to handle large-scale datasets and their potential for transfer learning across different spam detection tasks. It highlights the role of deep learning in enhancing feature representation, model generalization, and the overall accuracy of spam detection systems

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9

Douzi, Samira, Feda A. AlShahwan, Mouad Lemoudden, and Bouabid El Ouahidi. "Hybrid Email Spam Detection Model Using Artificial Intelligence." International Journal of Machine Learning and Computing 10, no. 2 (February 2020): 316–22. http://dx.doi.org/10.18178/ijmlc.2020.10.2.937.

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10

Shweta B., Dand. "Survey on Spam Review Detection Using Spam Filtering Algorithms." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 10, 2021): 535–38. http://dx.doi.org/10.22214/ijraset.2021.36333.

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Generally the people trust on product on the basis of that product reviews and rating. Reviews can affect an organization or profile of a brand. The corporation has to assess market reactions towards its goods. However, it is not straightforward to track and organize popular reviews. Many public views are hard to manually process in social media. A methodology is then required to categories positive or negative public assessments automatically. Online feedback will provide customers with an insight into the consistency, efficiency and advice of the product; this provides prospective buyers with a better understanding of the product. One such unrealized opportunity is the usability of web assessments from suppliers in order to fulfill client requirements by evaluating beneficial feedback. Good and negative reviews play a major role in assessing customer needs and in quicker collection of product input from consumers. Sentiment Analysis is a computer study that extracts contextual data from the text. In this study a vast number of online mobile telephone ratings are analyzed. We classify the text as positive and negative, but we also included feelings of frustration, expectation, disgust, apprehension, happiness, regret, surprise and confidence for spam review detection. This delimited grouping of feedback helps to holistically assess the product, allowing buyers to decide better.

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Sowmya, D. "DETECTION OF SPAM REVIEWS IN WEBSITES USING NET SPAM ALGORITHM." International Journal of Engineering Applied Sciences and Technology 7, no. 11 (March 1, 2023): 46–50. http://dx.doi.org/10.33564/ijeast.2023.v07i11.008.

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Now-a-days, people in general choose to purchase an online item depending on the reviews and feedback given in social media. The probability of leaving a scrutiny gives a glorious chance for person who writing a spam reviews regarding the product for individual reasons. Categorizing these kinds of spammers and the spam content create an interesting issue of analysis. Though a generous range of studies are done recently towards this, till date the methodologies used still hardly find the spam reviews. Here, propose an exclusive framework called Net-Spam that exploit spam options for creating review datasets as heterogeneous data networks to map spam detection procedure into further classifications. Maltreatment of spam options helps us to get the best output pertaining to various metrics experimented on realtime review datasets from Amazon and Yelp websites. The results show that Net-Spam outperforms the current ways from among the three classes of features namely detection of review, detection of user and detection of Spam Groupers.

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12

Elizalde, Benjamin M., and Dimitra Emmanouilidou. "Audio-based spam call detection." Journal of the Acoustical Society of America 150, no. 4 (October 2021): A357. http://dx.doi.org/10.1121/10.0008583.

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Spam communications are organized attempts of falsified claims with the purpose of marketing, spreading false information and deceiving the end recipient. Phone spam is an international nuisance, with the U. S. among the most spammed countries in the world in 2020. In addition to the agitating nature of these calls, criminal scams are defrauding subscribers of billions of dollars every year. Therefore, it is necessary to develop automated systems for the identification of spam calls to minimize fraud and reduce the displeasure of receiving them. The call origin, call duration and other Call Detail Records can be used to assess whether a call is fraudulent or not, but the actual audio content is overlooked. This work focuses on extracting acoustic features from voicemail recordings containing speech, which are used to train Machine Learning models that identify spam calls. Both local and global feature descriptors are used, including Mel-Frequency Cepstral Coefficients and Log-Mel Spectrum, and their efficacy for distinguishing spam from non-spam calls is explored. We demonstrate that a spam voice call can be detected while relying only on the acoustic information of the call. A further analysis of the temporal and spectral features that are most informative for the task is also presented.

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13

Abhinav, Chode. "Spam Mail Detection using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 2327–29. http://dx.doi.org/10.22214/ijraset.2022.44315.

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Abstract: Spam email is one of the most serious problems in the online world. Nowadays, a large portion of the population relies on available emails or communications from strangers. As a result, the fact that anyone can leave an email or a message opens the door for spammers to compose spam messages concerning our various interests. Spam fills up our inbox with unnecessary messages, slowing down our internet connection and stealing valuable information such as our contact information and accurate information. Detecting spammers and spam content is a major issue of research and time-consuming tasks. Email spam is when someone sends out a large number of emails in a short period of time. The purpose of spam filtering is to determine whether an email is spam or ham. With this proposed system the specified mail can be detected as spam or ham and also IP address of mail.

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14

Baig, Azhar. "Email Spam Detection using SVM." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (July 15, 2021): 669–72. http://dx.doi.org/10.22214/ijraset.2021.36383.

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E-mail contributes to internet messaging as a necessary component. Spam mails are unwanted messages that appear in large numbers and are exploited by spammers to divulge personal information of the user. These e-mails are frequently company/control announcements or malware that the user receives suddenly. Email spamming is one of the Internet's unsolved challenges, causing inconvenience to users and loss to businesses. Filtering is one of the foremost widely used and important methods for preventing spam emails. Email filters are commonly wont to organize incoming emails, protect computers from viruses, and eliminate spam. We present this method to classifying spam emails using support vector machines during this study, the SVM outperformed other classifiers.

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15

Abdul Kadir, Mohd Fadzil, Ahmad Faisal Amri Abidin, Mohamad Afendee Mohamed, and Nazirah Abdul Hamid. "Spam detection by using machine learning based binary classifier." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (April 1, 2022): 310. http://dx.doi.org/10.11591/ijeecs.v26.i1.pp310-317.

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<span lang="EN-US">Because <span lang="EN-US">of its ease of use and speed compared to other communication applications, email is the most commonly used communication application worldwide. However, a major drawback is its inability to detect whether mail content is either spam or ham. There is currently an increasing number of cases of stealing personal information or phishing activities via email. This project will discuss how machine learning can help in spam detection. Machine learning is an artificial intelligence application that provides the ability to automatically learn and improve data without being explicitly programmed. A binary classifier will be used to classify the text into two different categories: spam and ham. This research shows the machine learning algorithm in the Azure-based platform predicts the score more accurately compared to the machine learning algorithm in visual studio, hybrid analysis and JoeSandbox cloud.</span></span>

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16

Adewole, Kayode Sakariyah, Nor Badrul Anuar, Amirrudin Kamsin, and Arun Kumar Sangaiah. "SMSAD: a framework for spam message and spam account detection." Multimedia Tools and Applications 78, no. 4 (July 21, 2017): 3925–60. http://dx.doi.org/10.1007/s11042-017-5018-x.

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17

Larabi-Marie-Sainte, Souad, Sanaa Ghouzali, Tanzila Saba, Linah Aburahmah, and Rana Almohaini. "Improving spam email detection using deep recurrent neural network." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 3 (March 1, 2022): 1625. http://dx.doi.org/10.11591/ijeecs.v25.i3.pp1625-1633.

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<span>Nowadays the entire world depends on emails as a communication tool. Spammers try to exploit various vulnerabilities to attack users with spam emails. While it is difficult to prevent spam email attacks, many research studies have been developed in the last decade in an attempt to detect spam emails. These studies were conducted using machine learning techniques and various types of neural networks. However, with all their attempts the highest accuracy acquired was 94.2% by random forest classifier. Deep learning techniques have demonstrated higher accuracy performance compared to the traditional machine learning algorithms. In this paper, deep recurrent neural network was used to determine whether an email is a spam email. After investigating different configurations for this method, the best setting that generated the highest accuracy was based on using Tanh as the activation function with the dropout rate equals to 0.1 and the number of epochs achieving 100. The proposed approach attained a high accuracy of 99.7% which surpassed the best accuracy (98.7%) obtained by the hybrid gated recurrent unit recurrent neural network approach.</span>

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18

Shams, Amin, and Touraj Banirostam. "Incremental Learning for Spam Detection." IJARCCE 6, no. 1 (January 30, 2017): 1–6. http://dx.doi.org/10.17148/ijarcce.2017.6101.

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Himaja, S., Dr N. Chandra Sekhar Reddy, and S. Srinivas. "Secured Tagging [Redefining Spam Detection]." IOSR Journal of Computer Engineering 16, no. 2 (2014): 19–22. http://dx.doi.org/10.9790/0661-162111922.

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20

Sadigh, Alireza Naeimi, Sattar Hashemi, and Ali Hamzeh. "Spam Detection By Stackelberg Game." Advanced Computing: An International Journal 2, no. 2 (March 30, 2011): 32–40. http://dx.doi.org/10.5121/acij.2011.2203.

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21

Hans, Kanchan, Laxmi Ahuja, and S. K. Muttoo. "Approaches for Web Spam Detection." International Journal of Computer Applications 101, no. 1 (September 18, 2014): 38–44. http://dx.doi.org/10.5120/17655-8467.

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22

Spirin, Nikita, and Jiawei Han. "Survey on web spam detection." ACM SIGKDD Explorations Newsletter 13, no. 2 (May 2012): 50–64. http://dx.doi.org/10.1145/2207243.2207252.

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23

Tuan Vu, Minh, Quang Anh Tran, Frank Jiang, and Van Quan Tran. "Multilingual Rules for Spam Detection." Journal of Machine to Machine Communications 1, no. 2 (2015): 107–22. http://dx.doi.org/10.13052/jmmc2246-137x.122.

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24

DeBarr, Dave, and Harry Wechsler. "Spam detection using Random Boost." Pattern Recognition Letters 33, no. 10 (July 2012): 1237–44. http://dx.doi.org/10.1016/j.patrec.2012.03.012.

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Chaturved, S. Aditya, and Lalit Purohit. "Spam Message Detection: A Review." International Journal of Computing and Digital Systems 12, no. 3 (August 6, 2022): 439–51. http://dx.doi.org/10.12785/ijcds/120135.

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Annadatha, Annapurna, and Mark Stamp. "Image spam analysis and detection." Journal of Computer Virology and Hacking Techniques 14, no. 1 (October 14, 2016): 39–52. http://dx.doi.org/10.1007/s11416-016-0287-x.

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27

Kumar, Arju, Saurav Kumar, Kishan Kumar, and Dr Bharat Bhushan Naib. "E-mail Fraud Detection." International Journal of Emerging Science and Engineering 11, no. 9 (August 30, 2023): 1–7. http://dx.doi.org/10.35940/ijese.b7797.0811923.

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Spam issues have become worse on social media platforms and apps with the growth of IoT. To solve the problem, researchers have suggested several spam detection techniques. Spam rates are still high despite the use of anti-spam technologies and tactics, especially given the ubiquity of rogue e-mails that lead to dangerous websites. By using up memory or storage space, spam e-mails may cause servers to run slowly. One of the most essential methods for identifying and eliminating spam is filtering e-mails. To this end, various deep learning and machine learning technologies have been used, including Naive Bayes, decision trees, SVM, and random forest. E-mail and Internet of Things spam filters use various machine learning approaches and systems are categorized in this research. Additionally, as more people use mobile devices and SMS services become more affordable, the issue of spam SMS messages is spreading worldwide. This study suggests using a variety of machine learning approaches to detect and get rid of spam as a solution to this problem. According to the trial findings, the TF-IDF with Random Forest classification algorithm outperformed the other examined algorithms in accuracy %. It is only possible to gauge performance on accuracy since the dataset is imbalanced. Therefore, the algorithms must have good precision, recall, and F-measure.

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C, Saranya, Santosh Kumar, Lokesh S, and Ram Ratan. "Spam Detection on Social Media Platform." International Journal of Innovative Research in Advanced Engineering 10, no. 06 (June 23, 2023): 355–61. http://dx.doi.org/10.26562/ijirae.2023.v1006.20.

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Social Media has become an essential platform for communication and information sharing. With its widespread popularity, the problem of spam has also become increasingly common. In this paper, we propose an effective machine learning- based approach for spam detection on social media. Our approach uses various features and classifiers to distinguish spam messages from legitimate ones. The features used include lexical, syntactic, and semantic features, while the classifiers used include decision trees, naive Bayes, and support vector machines. Our experimental results demonstrate that the proposed approach outperforms existing spam detection methods on social media.

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Hussain, Naveed, Hamid Turab Mirza, Ghulam Rasool, Ibrar Hussain, and Mohammad Kaleem. "Spam Review Detection Techniques: A Systematic Literature Review." Applied Sciences 9, no. 5 (March 8, 2019): 987. http://dx.doi.org/10.3390/app9050987.

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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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Yao, Jinliang, Chenrui Wang, Chuang Hu, and Xiaoxi Huang. "Chinese Spam Detection Using a Hybrid BiGRU-CNN Network with Joint Textual and Phonetic Embedding." Electronics 11, no. 15 (August 3, 2022): 2418. http://dx.doi.org/10.3390/electronics11152418.

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The proliferation of spam in China has a negative impact on internet users’ experiences online. Existing methods for detecting spam are primarily based on machine learning. However, it has been discovered that these methods are susceptible to adversarial textual spam that has frequently been imperceptibly modified by spammers. Spammers continually modify their strategies to circumvent spam detection systems. Text with Chinese homophonic substitution may be easily understood by users according to its context. Currently, spammers widely use homophonic substitution to break down spam identification systems on the internet. To address these issues, we propose a Bidirectional Gated Recurrent Unit (BiGRU)–Text Convolutional Neural Network (TextCNN) hybrid model with joint embedding for detecting Chinese spam. Our model effectively uses phonetic information and combines the advantages of parameter sharing from TextCNN with long-term memory from BiGRU. The experimental results on real-world datasets show that our model resists homophone noise to some extent and outperforms mainstream deep learning models. We also demonstrate the generality of joint textual and phonetic embedding, which is applicable to other deep learning networks in Chinese spam detection tasks.

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Jany Shabu, S. L., V. Netaji Subhash Chandra Bose, Venkatesh Bandaru, Sardar Maran, and J. Refonaa. "Spam and Fake Spam Message Detection Framework Using Machine Learning Algorithm." Journal of Computational and Theoretical Nanoscience 17, no. 8 (August 1, 2020): 3444–48. http://dx.doi.org/10.1166/jctn.2020.9202.

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Online reviews about the acquisition of items or administrations gave have become the primary wellspring of clients’ conclusions. So as to pick up benefit or acclaim, as a rule spam reviews are composed to advance or downgrade a couple of target items or administrations. This training is known as review spamming. In the previous barely any years, an assortment of strategies have been proposed so as to illuminate the issue of spam reviews. It is a mainstream correspondence and furthermore known as information trade media. Information could be of a book, numbers, figures or insights that are gotten to by a PC. These days, numerous individuals relies upon substance accessible in web-based social networking in their choices. Sharing of data with people groups has additionally pulled in social spammers to endeavor and spread spam messages to advance individual web logs, notices, advancements, phishing, trick, fakes, etc. The possibility that anyone will leave a review give a brilliant possibility for spammers to post spit audit with respect to item and administrations for different interests and possibilities. In this way, we propose a fake message detection system utilizing ML to recognize the spam and fake messages on the internet based life stage.

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Revathi, G., K. Nageswara Rao, and G. Sita Ratnam. "Email Spam Detection using Naïve Bayes Algorithm." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (September 30, 2022): 653–55. http://dx.doi.org/10.22214/ijraset.2022.46654.

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Abstract: Email Spam has become a vital issue currently, with high-speed growth of internet users. Some people are using them for illegal conducts, phishing and fraud. Sending malicious link through spam emails which can harm our system and may also they will seek into our system. The need of email spam detection is to prevent spam messages from lagging into user’s inbox so it’ll improve user experience. This project will identify those spam emails by using machine learning approach. Machine learning is one amongst the applications of Artificial Intelligence that allow systems to read and improve from experience without being specific programmed. This paper will discuss the machine learning algorithm which is Naïve Bayes. It is a probabilistic classifier, which means it predicts on the idea of the probability of an object and it is selected for the email spam detection having best precision and accuracy.

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Nivedha, M. A., and S. Raja. "Detection of Email Spam using Natural Language Processing Based Random Forest Approach." International Journal of Computer Science and Mobile Computing 11, no. 2 (February 28, 2022): 7–22. http://dx.doi.org/10.47760/ijcsmc.2022.v11i02.002.

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An unsolicited means of digital communications in the internet world is the spam email, which could be sent to an individual or a group of individuals or a company. These spam emails may cause serious threat to the user i.e., the email addresses used for any online registrations may be collected by the malignant third parties (spammers) and they expose the genuine user to various kinds of attacks. Another method of spamming is by creating a temporary email register and receive emails that can be terminated after some certain amount of time. This method is well suited for misusing those temporary email addresses for sending free spam emails without revealing the spammers real account details. These attacks create major problems like theft of user credentials, lack of storage, etc. Hence it is essential to introduce an efficient detection mechanism through feature extraction and classification for detecting spam emails and temporary email addresses. This can be accomplished through a novel Natural Language Processing based Random Forest (NLP-RF) approach. With the help of our proposed approach, the spam emails are reduced and this method improves the accuracy of spam email filtering, since the use of NLP makes the system to detect the natural languages spoken by people and the Random Forest approach uses multiple decision trees and uses a random node for filtering the spams.

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Abuzaid, Nawal. "Image SPAM Detection Using ML and DL Techniques." International Journal of Advances in Soft Computing and its Applications 14, no. 1 (March 28, 2022): 227–43. http://dx.doi.org/10.15849/ijasca.220328.15.

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Abstract Since e-mail is one of the most common places to send messages, spammers have, in recent years, targeted it as a preferred way of distributing undesired messages (spam) to several users to spread viruses, cause destruction, and obtain user's information. Spam images are considered one of the known spam types. The spammer processes images and changes their characteristics, especially background colour, font type, or adding artefacts to the images to spread spam. In this paper, we proposed a spam detection model using Several ML (Random-Forest (RF), Decision-Tree (DT), KNearest Neighbor (KNN), Support-Vector Machine (SVM), NaïveBays (NB), and Convolutional Neural Network (CNN)). Several experiments evaluate the efficiency and performance of the (ML) algorithms for spam detection. Using the Image Spam Hunter Dataset extracted from real spam e-mails, the proposed model achieved over 99% accuracy on spam image detection. Keywords: SPAM, Machine Learning, Image Classification, Feature Extraction, Deep Learning.

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Kaddoura, Sanaa, Ganesh Chandrasekaran, Daniela Elena Popescu, and Jude Hemanth Duraisamy. "A systematic literature review on spam content detection and classification." PeerJ Computer Science 8 (January 20, 2022): e830. http://dx.doi.org/10.7717/peerj-cs.830.

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The presence of spam content in social media is tremendously increasing, and therefore the detection of spam has become vital. The spam contents increase as people extensively use social media, i.e., Facebook, Twitter, YouTube, and E-mail. The time spent by people using social media is overgrowing, especially in the time of the pandemic. Users get a lot of text messages through social media, and they cannot recognize the spam content in these messages. Spam messages contain malicious links, apps, fake accounts, fake news, reviews, rumors, etc. To improve social media security, the detection and control of spam text are essential. This paper presents a detailed survey on the latest developments in spam text detection and classification in social media. The various techniques involved in spam detection and classification involving Machine Learning, Deep Learning, and text-based approaches are discussed in this paper. We also present the challenges encountered in the identification of spam with its control mechanisms and datasets used in existing works involving spam detection.

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Bhavsar, Darshan, Abhishek Chandekar, Prof Chetana Patil, Mihir Chaudhari, and Stephen Dcruz. "SURVEY: YOUTUBE SPAM COMMENTS DETECTION AND DELETION." International Journal of Engineering Applied Sciences and Technology 7, no. 7 (November 1, 2022): 40–42. http://dx.doi.org/10.33564/ijeast.2022.v07i07.007.

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The quality of online social platforms has risen, and spammers have developed a number of strategies to trick consumers into clicking on harmful links. This is accomplished by posting spam to various social media networks' comment sections. This paper suggests a method for identifying spam comments on YouTube, which have grown significantly recently. YouTube operates a spam blocking system, although it consistently fails to do so effectively so our program tries to tackle the problem by not only detecting but also deleting the spam comments by using Machine Learning

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Hameed, Sarab M. "Differential evolution detection models for SMS spam." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (February 1, 2021): 596. http://dx.doi.org/10.11591/ijece.v11i1.pp596-601.

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With the growth of mobile phones, short message service (SMS) became an essential text communication service. However, the low cost and ease use of SMS led to an increase in SMS Spam. In this paper, the characteristics of SMS spam has studied and a set of features has introduced to get rid of SMS spam. In addition, the problem of SMS spam detection was addressed as a clustering analysis that requires a metaheuristic algorithm to find the clustering structures. Three differential evolution variants viz DE/rand/1, jDE/rand/1, jDE/best/1, are adopted for solving the SMS spam problem. Experimental results illustrate that the jDE/best/1 produces best results over other variants in terms of accuracy, false-positive rate and false-negative rate. Moreover, it surpasses the baseline methods.

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Shen, Hua, Xinyue Liu, and Xianchao Zhang. "A Detection Method for Social Network Images with Spam, Based on Deep Neural Network and Frequency Domain Pre-Processing." Electronics 11, no. 7 (March 29, 2022): 1081. http://dx.doi.org/10.3390/electronics11071081.

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As a result of the rapid development of internet technology, images are widely used on various social networks, such as WeChat, Twitter or Facebook. It follows that images with spam can also be freely transmitted on social networks. Most of the traditional methods can only detect spam in the form of links and texts; there are few studies on detecting images with spam. To this end, a novel detection method for identifying social images with spam, based on deep neural network and frequency domain pre-processing, is proposed in this paper. Firstly, we collected several images with embedded spam and combined the DIV2K2017 dataset to build an image dataset for training the proposed detection model. Then, the specific components of the spam in the images were determined through experiments and the pre-processing module was specially designed. Low-frequency domain regions with less spam are discarded through Haar wavelet transform analysis. In addition, a feature extraction module with special convolutional layers was designed, and an appropriate number of modules was selected to maximize the extraction of three different high-frequency feature regions. Finally, the different high-frequency features are spliced along the channel dimension to obtain the final classification result. Our extensive experimental results indicate that the spam element mainly exists in the images as high-frequency information components; they also prove that the proposed model is superior to the state-of-the-art detection models in terms of detection accuracy and detection efficiency.

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Dar, Momna, Faiza Iqbal, Rabia Latif, Ayesha Altaf, and Nor Shahida Mohd Jamail. "Policy-Based Spam Detection of Tweets Dataset." Electronics 12, no. 12 (June 14, 2023): 2662. http://dx.doi.org/10.3390/electronics12122662.

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Spam communications from spam ads and social media platforms such as Facebook, Twitter, and Instagram are increasing, making spam detection more popular. Many languages are used for spam review identification, including Chinese, Urdu, Roman Urdu, English, Turkish, etc.; however, there are fewer high-quality datasets available for Urdu. This is mainly because Urdu is less extensively used on social media networks such as Twitter, making it harder to collect huge volumes of relevant data. This paper investigates policy-based Urdu tweet spam detection. This study aims to collect over 1,100,000 real-time tweets from multiple users. The dataset is carefully filtered to comply with Twitter’s 100-tweet-per-hour limit. For data collection, the snscrape library is utilized, which is equipped with an API for accessing various attributes such as username, URL, and tweet content. Then, a machine learning pipeline consisting of TF-IDF, Count Vectorizer, and the following machine learning classifiers: multinomial naïve Bayes, support vector classifier RBF, logical regression, and BERT, are developed. Based on Twitter policy standards, feature extraction is performed, and the dataset is separated into training and testing sets for spam analysis. Experimental results show that the logistic regression classifier has achieved the highest accuracy, with an F1-score of 0.70 and an accuracy of 99.55%. The findings of the study show the effectiveness of policy-based spam detection in Urdu tweets using machine learning and BERT layer models and contribute to the development of a robust Urdu language social media spam detection method.

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Gong, Song Jie, and Qiu Yuan Sheng. "Research on Review Spam Detection Based on Sentiment Analysis in Electronic Commerce." Applied Mechanics and Materials 602-605 (August 2014): 2101–4. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.2101.

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With the wide adoption of the computer and network communication technology in our daily life, electronic business technology has seen a rapid development all over the world. It is common for electronic commerce websites to enable their customers to write reviews of products that they have purchased. Unfortunately, reviewers may write some untruthful opinions in order to promote or damage specific products reputation which called review spam. Product review spam detection makes an attempt to find untruthful opinions. In order to find the review spammers, the paper presents a review spam detecting based on sentiment analysis. Three feature behaviors of review spammers are recognized, including targeting at product type, targeting at product brand and targeting at product seller. The review spam detecting method based on sentiment analysis is suitable for detecting review spam, and is efficient and effective.

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Sharapova, E. V., and R. V. Sharapov. "Detection of spam using email signatures." Information Technology and Nanotechnology, no. 2416 (2019): 165–72. http://dx.doi.org/10.18287/1613-0073-2019-2416-165-172.

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Currently, unwanted emails are actively sent to the Internet. Millions copies of emails are sent simultaneously to various users. Often e-mails undergo minor modifications to complicate the detection of spam. The paper proposes options for determining the signature of e-mails that allow identify letters with the same content and structure. Content signature of the letter includes the basic phrases in the text of the e-mail with the exception of names, numeric codes, suspicious words that are not included in the dictionary. Structure signatures incorporate the same type of e-mails, such as paragraphs, tables, images. The paper shows the results of using signatures to detect e-mail spam.

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S, Sarju, Riju Thomas, and Emilin Shyni C. "Spam Email Detection using Structural Features." International Journal of Computer Applications 89, no. 3 (March 1, 2014): 38–41. http://dx.doi.org/10.5120/15485-4265.

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., Pooja, and Komal Kumar Bhatia. "Spam Detection using Naive Bayes Classifier." International Journal of Computer Sciences and Engineering 6, no. 7 (July 31, 2018): 712–16. http://dx.doi.org/10.26438/ijcse/v6i7.712716.

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., Pooja, and Komal Kumar Bhatia. "Spam Detection using Naive Bayes Classifier." International Journal of Computer Sciences and Engineering 6, no. 7 (July 31, 2018): 934–38. http://dx.doi.org/10.26438/ijcse/v6i7.934938.

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Yang, Xiao Lei, Yi Dan Su, and Jin Ping Mo. "LSSVM-Based Social Spam Detection Model." Advanced Materials Research 765-767 (September 2013): 1281–86. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.1281.

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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.

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A, Stella, Deepthi K, Karthik D S, Mohammed Maaz Ahmed, and Mohak Pal. "REVIEW SPAM DETECTION USING MACHINE LEARNING." International Research Journal of Computer Science 9, no. 8 (August 13, 2022): 256–61. http://dx.doi.org/10.26562/irjcs.2022.v0908.20.

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Reviews, ratings, and experience stories left by customers on e-commerce websites and other online services are helpful to both buyers and sellers. The reviewer can foster more brand loyalty and aid in the understanding of other consumers' product experiences. Similar to how reviews help customers earn more profiles, reviews help businesses sell more things by enhancing customer satisfaction. However, suppliers may, unfortunately, abuse these review processes. For instance, one might fabricate positive evaluations to boost the reputation of a brand or attempt to denigrate rival brands' goods by posting fictitious reviews about them. Utilizing various machine learning techniques and tools are examples of existing solutions with supervision. Unlike previous work, I decided to use a wide range of vocabulary to work with, such as many datasets integrated into one enormous data set, rather than a confined dataset. Based on the reviews' text and emoji usage, sentiment analysis has been implemented. Review fraud is recognised and classed. The use of the Linear SVC, Support Vector Machine, and Random Forest algorithms yields the test results.

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Abd El-Kareem, Mohamed, Ayman Elshenawy, and Fawzi Elrfaey. "MAIL SPAM DETECTION USING STACKING CLASSIFICATION." Journal of Al-Azhar University Engineering Sector 12, no. 45 (October 1, 2017): 1242–55. http://dx.doi.org/10.21608/auej.2017.19151.

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Giyanani, Rohit, and Mukti Desai. "Spam Detection using Natural Language Processing." IOSR Journal of Computer Engineering 16, no. 5 (2014): 116–19. http://dx.doi.org/10.9790/0661-1654116119.

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Anupriya, Koneru, Kurakula Harini, Kethe Balaji, and Karnati Geetha Sudha. "Spam Mail Detection Using Optimization Techniques." Ingénierie des systèmes d information 27, no. 1 (February 28, 2022): 157–63. http://dx.doi.org/10.18280/isi.270119.

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On account of the widespread availability of internet access, email correspondence is one among the most well-known cost-effective and convenient method for users in the office and in business. Many people abuse this convenient mode of communication by spamming others with conciseness bulk emails. They use emails to collect personal information of the users to benefit financially. A literature review is conducted to investigate the most effective strategies for achieving successful outcomes while working with various spam mail datasets. K-Nearest Neighbor, Support Vector Machine, Naive Bayes, Decision Tree, Random Forest, and Logistic Regression are all employed in the implementation of machine learning techniques. To make classifiers more efficient, bio-inspired algorithms such as BAT and PSO are used. The accuracy of every classification algorithm along with and without optimization is observed. Factors such as accuracy, f1-score, precision, and recall are used to compare the results. This work is implemented in Python along with GUI interface Tkinter.

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Gupta, Suparna Das, Soumyabrata Saha, and Suman Kumar Das. "SMS Spam Detection Using Machine Learning." Journal of Physics: Conference Series 1797, no. 1 (February 1, 2021): 012017. http://dx.doi.org/10.1088/1742-6596/1797/1/012017.

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