计算机科学
恶意软件
网络钓鱼
计算机安全
黑客
概率逻辑
互联网隐私
文字袋模型
班级(哲学)
垃圾邮件程序
朴素贝叶斯分类器
万维网
人工智能
机器学习
垃圾邮件
互联网
支持向量机
作者
Maksim Sharabov,Georgi Tsochev,Vеskа Gаnchеvа,Antoniya Tasheva
出处
期刊:Electronics
[Multidisciplinary Digital Publishing Institute]
日期:2024-01-16
卷期号:13 (2): 374-374
标识
DOI:10.3390/electronics13020374
摘要
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 and effective defense mechanism within academic institutions becomes increasingly imperative. In exploring potential solutions, this study delves into the applicability and efficacy of Bayesian filters, a class of probabilistic classifiers renowned for their aptitude in distinguishing between legitimate emails and spam messages. Bayesian filters utilize statistical algorithms to analyze email content, learning patterns and features to accurately categorize incoming emails.
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