Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text. [PDF]
Complex Intell Systems, 2022 Shaaban MA, Hassan YF, Guirguis SK.
europepmc +2 more sources
SMS SPAM DETECTION WITH MULTINOMIAL NAIVE BAYES
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTSMS (short messaging service) usage has increased dramatically as a result of the growth in mobile users, enabling text messaging between smartphone and landline users. But there has also been a noticeable increase in unsolicited communications, or spam, coinciding with this growth in SMS usage.
openaire +1 more source
Machine Learning Sms Spam Detection Model
, 2019 Full textMillions of shillings are lost by mobile phone users every year in Kenya due to SMs Spam, a social engineering skill attempting to obtain sensitive information such as passwords, Personal identification numbers and other details by ...
Thiga, Moses +2 more
core +1 more source
The Cost Impact of Spam Filters: Measuring the Effect of Information System Technologies in Organizations [PDF]
More than 70% of global e-mail traffic consists of unsolicited and commercial direct marketing, also known as spam. Dealing with spam incurs high costs for organizations, prompting efforts to try to reduce spam-related costs by installing spam filters ...
Clement, Michel +3 more
core
A Hybrid Approach for Alluring Ads Phishing Attack Detection Using Machine Learning. [PDF]
Sensors (Basel), 2023 Shaukat MW +4 more
europepmc +1 more source
Design and Development of Antispammer for SMS Spam Detection [PDF]
, 2015 M.E. (Computer Science and Applications)The growth of the mobile phone users has led to a dramatic increase in SMS spam messages. Though in most parts of the world, mobile messaging channel is currently regarded as “clean” and trusted, on the contrast ...
Agarwal, Sakshi
core
A systematic literature review of cyber-security data repositories and performance assessment metrics for semi-supervised learning. [PDF]
Discov Data, 2023 Mvula PK +3 more
europepmc +1 more source
Imbalanced class distribution and performance evaluation metrics: A systematic review of prediction accuracy for determining model performance in healthcare systems. [PDF]
PLOS Digit Health, 2023 Owusu-Adjei M +3 more
europepmc +1 more source
MODEL EVALUATION FOR EVASIVE SMS SPAM DETECTION
This paper investigates different machine learning methods for identifying deceptive SMS spam.It is quite difficult to detect evasive spam communications because they use obfuscation to bypass typical filters. Some of the models that were assessed were Deep Learning, Naïve Bayes, Decision Trees, and Support Vector Machines.
openaire +1 more source
SMS Spam Detection Using Hybrid Deep Learning
The unprecedented increase of spam and promotional SMS messages has created the new challenge of quickly and accurately detecting these messages in the field of information security and communication. Users lose time and money to these spam messages, and
Saad, Z. M. (Zahraa) +1 more
core +1 more source