| Autors: Sharabov, M. Z., Tsochev, G. R., G, V. S., Tasheva, A. T. Title: Filtering and Detection of Real-Time Spam Mail Based on a Bayesian Approach in University Networks Keywords: spam; Bayesian network; university network; phishing email Abstract: With the advent of digital technologies as an integral part of today’s everyday life, the risk of information security breaches is increasing. Email spam, commonly known as junk email, continues to pose a significant challenge in the digital realm, inundating inboxes with unsolicited and often irrelevant messages. This relentless influx of spam not only disrupts user productivity but also raises security concerns, as it frequently serves as a vehicle for phishing attempts, malware distribution, and other cyber threats. The prevalence of spam is fueled by its low-cost dissemination and its ability to reach a wide audience, exploiting vulnerabilities in email systems. This paper marks the inception of an in-depth investigation into the viability and potential implementation of a robust spam filtering and prevention system tailored explicitly to university networks. With the escalating threat of email-based hacking attacks and the incessant deluge of spam, the need for a comprehensive.. References Issue
|
Цитирания (Citation/s):
1. Al Shammari, Ghadeer, and Nargis Parveen. "A Dual-Channel Deep Learning Framework For Real-Time Detection Of Zero-Day Attacks Using Cnn-Lstm Hybrid Networks." Nanotechnology Perceptions (2024): 1410-1440. - 2024 - в издания, индексирани в Scopus или Web of Science
2. Kachoukh, Amani, Naseer S. Albalawi, and Sami Aziz Alshammari. "Advanced Convolutional Neural Network and Long Short-Term Memory Model for Real-Time Spam Detection in Internet of Things Devices." J. Electrical Systems 20.10s (2024): 6114-6135. - 2024 - в издания, индексирани в Scopus или Web of Science
Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus и Web of Science
