Results 81 to 90 of about 2,863,518 (175)

Spam or Ham? A Hybrid Deep Learning Approach for SMS Spam Detection

International Journal for Research in Applied Science and Engineering Technology
With the advent of digital communications, SMS spam has also become a widespread issue, which is inconvenient and even threatening to users. In this project, we advocate a hybrid spam detection model combining Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks and TF-IDF, and efficiently leverages deep learning and text ...
openaire   +1 more source

Unified AI Models for Network Security on Edge Devices

International Journal of Computational Intelligence Systems
Rapidly identifying and mitigating security threats to reduce the impact of attacks is one of the most pressing challenges of our time. Digital threats frequently jeopardize users, often manifested as web-based, intranetwork, or spam-related attacks. The
Sengul Bayrak, Alper Karaca, Ferhat Toson, Mehmet Emin Tayfur, Selcuk Yavas   +4 more
doaj   +1 more source

Spam SMS Detection Using Machine Learning

INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Abstract - In today's digital communication era, unsolicited and malicious text messages, commonly known as spam, pose a significant threat to user privacy and mobile security. This project aims to develop an intelligent and automated system for SMS spam detection using machine learning techniques, with a focus on the Support Vector Machine (SVM ...
openaire   +1 more source

Instagram Spam Detection (ISD) [PDF]

An Instagram spam detection project would cover the project's aim, methods, and outcomes. It would detail how the project identifies and filters out spam content on Instagram to enhance user experience and security.
Prof. Anupam, Chaube, Pratik, Dewalker, Pranali V., Dhote, Rima, Ramteke, Anjali R., Patel   +4 more
core  

Exploiting Machine Learning to Subvert Your Spam Filter [PDF]

, 2008
Using statistical machine learning for making security decisions introduces new vulnerabilities in large scale systems. This paper shows how an adversary can exploit statistical machine learning, as used in the SpamBayes spam filter, to render it useless—
Nelson, Blaine, Tygar, J. D., Xia, Kai, Rubinstein, Benjamin I. P., Sutton, Charles, Barreno, Marco, Chi, Fuching Jack, Joseph, Anthony D., Saini, Udam   +8 more
core  

Multi-Modal Comparative Analysis on Execution of Phishing Detection Using Artificial Intelligence

IEEE Access
Phishing is the process of deceiving or stealing private or confidential information through illicit means. This could lead to financial loss, loss of reputation, and identity theft.
Divya Jennifer Dsouza, Anisha P. Rodrigues, Roshan Fernandes   +2 more
doaj   +1 more source

SMS sentiment classification using an evolutionary optimization based fuzzy recurrent neural network. [PDF]

Multimed Tools Appl, 2023
Srinivasarao U, Sharaff A.
europepmc   +1 more source

Towards eradication of SPAM: A study on intelligent adaptive SPAM filters [PDF]

, 2006
As the massive increase of electronic mail (email) usage continues, SPAM (unsolicited bulk email), has continued to grow because it is a very inexpensive method of advertising.
Hassan, Tarek
core  

A Hybrid Model with New Word Weighting for Fast Filtering Spam Short Texts. [PDF]

Sensors (Basel), 2023
Xia T, Chen X, Wang J, Qiu F.
europepmc   +1 more source

A Review of SMS Spam Detection

International Research Journal of Modernization in Engineering Technology and Science, 2023
openaire   +1 more source