Authors :
Praveen Kumar Vemula; Sri Charitha Veeranki; Monika Chowdary Mannem; Bala Satya Sai Pranathi Reddy; Dr. Sammy F
Volume/Issue :
Volume 9 - 2024, Issue 6 - June
Google Scholar :
https://tinyurl.com/vw4x43k3
Scribd :
https://tinyurl.com/2c7d7a6z
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24JUN1024
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
With the rapid surge of spam across the
Internet and its various forms, effectively identifying
and combating spam has become an urgent priority.
Cloud computing offers significant advantages in terms
of storage and processing capabilities, making it a viable
solution for analysing vast amounts of email data. To
address the dynamic nature of spam and its life cycle, an
anti-spam system with feedback reassessment is
proposed. This system incorporates a text filtering
approach based on active learning, involving four key
stages: training, filtering, feedback, and re-filtering.
Compared to traditional systems, the feedback-enabled
filtering system demonstrates improved keyword
filtering. To further enhance the accuracy of spam
detection and minimize misjudgements in legitimate
emails, leveraging weighted decision-making based on
email header information is recommended. Additionally,
for emails with sparse content, employing title weighting
in the filtering process proves to be both feasible and
effective, particularly in identifying spam with minimal
text content. Given the advancements of cloud-based
filtering methods over traditional algorithms, leveraging
cloud computing holds promise in effectively combating
the escalating volume of spam. As such, this paper delves
into an in-depth exploration of spam identification
within cloud computing environments, focusing on text
filtering systems. This study is informed by a
comprehensive analysis of existing anti-spam
technologies, aiming to contribute to the ongoing efforts
in mitigating spam proliferation online.
Keywords :
Cloud Computing,Cloud Security.
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With the rapid surge of spam across the
Internet and its various forms, effectively identifying
and combating spam has become an urgent priority.
Cloud computing offers significant advantages in terms
of storage and processing capabilities, making it a viable
solution for analysing vast amounts of email data. To
address the dynamic nature of spam and its life cycle, an
anti-spam system with feedback reassessment is
proposed. This system incorporates a text filtering
approach based on active learning, involving four key
stages: training, filtering, feedback, and re-filtering.
Compared to traditional systems, the feedback-enabled
filtering system demonstrates improved keyword
filtering. To further enhance the accuracy of spam
detection and minimize misjudgements in legitimate
emails, leveraging weighted decision-making based on
email header information is recommended. Additionally,
for emails with sparse content, employing title weighting
in the filtering process proves to be both feasible and
effective, particularly in identifying spam with minimal
text content. Given the advancements of cloud-based
filtering methods over traditional algorithms, leveraging
cloud computing holds promise in effectively combating
the escalating volume of spam. As such, this paper delves
into an in-depth exploration of spam identification
within cloud computing environments, focusing on text
filtering systems. This study is informed by a
comprehensive analysis of existing anti-spam
technologies, aiming to contribute to the ongoing efforts
in mitigating spam proliferation online.
Keywords :
Cloud Computing,Cloud Security.