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Effect of Spam Filter on SPOT Algorithm

Published: 10 August 2015 Publication History

Abstract

Compromised machine is any computing resource whose availability, confidentiality, integrity has been negatively impacted either intentionally or unintentionally, by an untrusted source. These machines are often used to elevate various security attacks such as DDoS (attempt to make a machine resource unavailable to its intended users), spamming and identity theft (practice of using other person's name and personal information). The most important of these attacks are spamming wherein these machines are used to send large chunks of unsolicited mails called as spam. To detect these unwanted and unsolicited email and prevent them from getting into the user's inbox we use Spam filter. To detect the machine as compromised, SPOT algorithm has been designed. In this paper, we compare the effect of different types of spam filters on the SPOT algorithm on finding the compromised machines.

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  • (2023)Comparison of machine learning techniques for spam detectionMultimedia Tools and Applications10.1007/s11042-023-14689-382:19(29227-29254)Online publication date: 20-Feb-2023

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Published In

cover image ACM Other conferences
WCI '15: Proceedings of the Third International Symposium on Women in Computing and Informatics
August 2015
763 pages
ISBN:9781450333610
DOI:10.1145/2791405
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 August 2015

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Author Tags

  1. Compromised Machines
  2. SPOT
  3. SPRT
  4. Spam Filters

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  • Refereed limited

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WCI '15

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WCI '15 Paper Acceptance Rate 98 of 452 submissions, 22%;
Overall Acceptance Rate 98 of 452 submissions, 22%

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  • (2023)Comparison of machine learning techniques for spam detectionMultimedia Tools and Applications10.1007/s11042-023-14689-382:19(29227-29254)Online publication date: 20-Feb-2023

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