Results 21 to 30 of about 2,863,518 (175)

SMS Spam Classification–Simple Deep Learning Models With Higher Accuracy Using BUNOW And GloVe Word Embedding

Journal of Applied Science and Engineering, 2023
Unwanted text messages are called Spam SMSs. It has been proven that Machine Learning Models can categorize spam messages efficiently and with great accuracy. However, the lack of proper spam filtering software or misclassification of genuine SMS as spam
Surajit Giri, Sayak Das, Sutirtha Bharati Das, Siddhartha Banerjee   +2 more
doaj   +1 more source

Persian SMS Spam Detection using Machine Learning and Deep Learning Techniques [PDF]

International Journal of Web Research, 2022
Spams are well-known examples of unsolicited text or messages which are sent by unknown individuals and cause issues for smartphone users. The inconvenience imposed on users, the loss of network traffic, the rise in the calculated cost, occupying more ...
Roya Khorashadizade, Somayyeh Jafarali Jassbi, Alireza Yari   +2 more
doaj   +1 more source

Detecting SMS Spam in the Age of Legitimate Bulk Messaging [PDF]

Proceedings of the 9th ACM Conference on Security & Privacy in Wireless and Mobile Networks, 2016
Text messaging is used by more people around the world than any other communications technology. As such, it presents a desirable medium for spammers. While this problem has been studied by many researchers over the years, the recent increase in legitimate bulk traffic (e.g., account verification, 2FA, etc.) has dramatically changed the mix of traffic ...
Bradley Reaves, Logan Blue, Dave Tian, Patrick Traynor, Kevin R. B. Butler   +4 more
openaire   +2 more sources

BlogForever D2.5: Weblog spam filtering report and associated methodology [PDF]

, 2013
This report is written as a first attempt to define the BlogForever spam detection strategy. It comprises a survey of weblog spam technology and approaches to their detection.
Kasioumis, Nikolaos, Stepanyan, K., Stepanyan, Karen, Banos, V., Rynning, Morten, Kopidaki, S., Ross, Seamus, Kim, Y., Kopidaki, Stella, Rynning, M., Banos, Vangelis, Ross, S., Kasioumis, N., Kim, Yunhyong   +13 more
core   +1 more source

Comparative study on SMS spam message detection with different machine learning methods for safety communication

, 2022
Over recent years, the utilisation of short message service (SMS) has been growing significantly. Along with it, there is a notable increase in spam messages from spammers.
Muthusamy, S, Mirjalili, S, Raja, SKS, Vishnuhari, R, Krishnamoorthy, N, Vignesh, M   +5 more
core   +1 more source

An Intelligent Framework Based on Deep Learning for SMS and e-mail Spam Detection

Applied Computational Intelligence and Soft Computing, 2023
The use of short message service (SMS) and e-mail have increased too much over the last decades. 80% of people do not read e-mails while 98% of cell phone users daily read their SMS. However, these communication media are unsafe and can produce malicious
Umair Maqsood, Saif Ur Rehman, Tariq Ali, Khalid Mahmood, Tahani Alsaedi, Mahwish Kundi   +5 more
doaj   +1 more source

Application of evolutionary algorithms in detecting SMS spam at access layer [PDF]

Proceedings of the 13th annual conference on Genetic and evolutionary computation, 2011
In recent years, Short Message Service (SMS) has been widely exploited in arbitrary advertising campaigns and the propagation of scam. In this paper, we first analyze the role of SMS spam as an increasing threat to mobile and smart phone users. Afterward, we present a filtering method for controlling SMS spam on the access layer of mobile devices.
M. Zubair Rafique, Nasser Alrayes, Muhammad Khurram Khan   +2 more
openaire   +2 more sources

A Review on Mobile SMS Spam Filtering Techniques

IEEE Access, 2017
Under short messaging service (SMS) spam is understood the unsolicited or undesired messages received on mobile phones. These SMS spams constitute a veritable nuisance to the mobile subscribers.
Shafi'I Muhammad Abdulhamid, Muhammad Shafie Abd Latiff, Haruna Chiroma, Oluwafemi Osho, Gaddafi Abdul-Salaam, Adamu I. Abubakar, Tutut Herawan   +6 more
doaj   +1 more source

SMS Spam Detection using Supervised Learning

, 2021
Over the last decade, the growth of short message services has been rising. These text messages are more powerful for corporations than even SMS. This is because about 80 percent of sms remain unopened while 98 percent of smartphone users read theirs by ...
et. al., Naveen Chaurasia,
core   +1 more source

SMS Spam Detection using Machine Learning

International Journal of Advanced Research in Science, Communication and Technology
Emails are a crucial instrument for achieving the interconnectedness of machines and people worldwide in the global age we live in, when the world is becoming smaller and smaller and everyone is connected to one another. Since the development of communication technology and the exponential increase in internet usage in recent years, emails have become ...
null Phanirama Prasad, null G Shivaraj, null J M Renuka, null Janardan Kulkarni, null H Aishwarya   +4 more
  +5 more sources