Detection of Social Network Spam Based on Improved Extreme Learning Machine

Zhang, Zhijie; Hou, Rui; Yang, Jin Ho

doi:10.1109/access.2020.3002940

Cited by 41 publications

(16 citation statements)

References 37 publications

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“…The results of this approach show that it provides better accuracy in spam detection when comparing with Bag-of-Wordsand semantic contents [17].The unbalanced data distribution caused by spam and non-spam messages in Twitter is solved by analyzing spam characteristics from Twitter by using Improved Incremental Fuzzy kernel regularized Extreme Learning Machine (I2FELM) for accurately detecting Twitter spam. This I2FELM detects the balanced and unbalanced dataset efficiently with few characteristics, thus proves its effectiveness in spam detection [18].…”

Section: Related Workmentioning

confidence: 61%

Detection of Email Spam using Natural Language Processing Based Random Forest Approach

Rayan

Taloba

2021

Preprint

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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 mechanismthrough 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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Section: Related Workmentioning

confidence: 61%

Detection of Email Spam using Natural Language Processing Based Random Forest Approach

Rayan

Taloba

2021

Preprint

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“…Focusing on the uneven distribution of spam data and nonspam data on Twitter, Zhang et al [70] proposed an algorithm named I2RELM (Improved Incremental Fuzzy-kernelregularized Extreme Learning Machine), which adopt fuzzy weights (each input data is provided with a weight s i , which is in the interval of (0,1] and assigned by the ratio of spam users to non-spam users in the whole dataset) to improve the detection accuracy of the model on the non-uniformly distributed dataset. They evaluated their method with the data obtained from Twitter, and the performance of I2RELM on the accuracy, TRP, precision, and F-measure was superior to SVM, DT, RF, BP, RBF, ELM, and XG-Boost.…”

Section: Spam Detectionmentioning

confidence: 99%

Adversarial Machine Learning on Social Network: A Survey

Guo

2021

Front. Phys.

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In recent years, machine learning technology has made great improvements in social networks applications such as social network recommendation systems, sentiment analysis, and text generation. However, it cannot be ignored that machine learning algorithms are vulnerable to adversarial examples, that is, adding perturbations that are imperceptible to the human eye to the original data can cause machine learning algorithms to make wrong outputs with high probability. This also restricts the widespread use of machine learning algorithms in real life. In this paper, we focus on adversarial machine learning algorithms on social networks in recent years from three aspects: sentiment analysis, recommendation system, and spam detection, We review some typical applications of machine learning algorithms and adversarial example generation and defense algorithms for machine learning algorithms in the above three aspects in recent years. besides, we also analyze the current research progress and prospects for the directions of future research.

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“…These data create a great impact on the daily life of people in personal and working environment. The spam detection in social networks facilitates analyzes and event monitoring in social media and regulates them [18]. The spam mails are increasing which causes serious threats for years.…”

Section: Introductionmentioning

confidence: 99%

Detection of Email Spam using Natural Language Processing Based Random Forest Approach

Nivedha¹,

Raja²

2022

IJCSMC

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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.

show abstract

Detection of Social Network Spam Based on Improved Extreme Learning Machine

Cited by 41 publications

References 37 publications

Detection of Email Spam using Natural Language Processing Based Random Forest Approach

Detection of Email Spam using Natural Language Processing Based Random Forest Approach

Adversarial Machine Learning on Social Network: A Survey

Detection of Email Spam using Natural Language Processing Based Random Forest Approach

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