PDF가 표시되지 않으면 여기를 클릭하여 다운로드

1
Alkhdour, T., Alrawashdeh, R., Almaiah, M., Alali, R., Salloum, S., and Aldahyani, T. H. (2024), “A new technique for detecting email spam risks using LSTM-particle swarm optimization algorithms,” Journal of Theoretical and Applied Information Technology, 102(14). Google Scholar ›
2
Androutsopoulos, I, Paliouras, G., Karkaletsis, V., Sakkis, G., Spyropoulos, C. D., and Stamatopoulos, P. (2000), “Learning to Filter Spam E-Mail: A Comparison of a Naive Bayesian and a Memory-Based Approach,” in Proceedings of the 4th European Conference on Principles and Practice of Knowledge Discovery in Databases PKDD. Google Scholar ›
3
Androutsopoulos, I., Koutsias, J., Chandrinos, K. V., and Spyropoulos, C. D. (2000), An Experimental Comparison of Naive Bayesian and Keyword-Based Anti-Spam Filtering with Personal E-Mail Messages. in Proceedings of the 23rd annual international ACM SIGIR conference, pp. 160-167. Google Scholar ›
4
Cavusoglu, H., Mishra, B., and Raghunathan, S. (2005). “The Value of Intrusion Detection Systems in Information Technology Security Architecture,” Information Systems Research, 16(1), pp. 28-46. Google Scholar ›
5
Chamaa, H. (2025). 30 Email Spam Statistics to Know in 2025. https://againstdata.com/. https://againstdata.com/blog/email-spam-statistics Google Scholar ›
6
Email ToolTester. (2024). Spam statistics. https://www.emailtooltester.com/en/blog/spam-statistics Google Scholar ›
7
Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), pp. 861-874. Google Scholar ›
8
Fawcett, T., and Provost, F. (1997). Adaptive Fraud Detection. Data Mining and Knowledge Discovery, 1, pp. 291–316. Google Scholar ›
9
FTC, F. T. C. (2007). Spam Summit: The Next Generation of Threats and Solutions. http://www.ftc.gov/os/2007/12/071220spamsummitreport.pdf Google Scholar ›
10
FTC, F. T. C. (2023). CAN-SPAM Act: A Compliance Guide for Business. https://www.ftc.gov/business-guidance/resources/can-spam-act-compliance-guide-business Google Scholar ›
11
González-Talaván, G. (2006). “A simple, configurable SMTP anti-spam filter: Greylists,” Computers & Security, 25(3), pp. 229-236. Google Scholar ›
12
Gray, A., and Haahr, M. (2005), Personalised, Collaborative Spam Filtering. in Proceedings of the First Conference on E-mail and Anti-Spam, Mountain View, California. Google Scholar ›
13
Hand, D. J. (2009), Evaluating diagnostic tests: The area under the ROC curve and the balance of errors. Statistics in Medicine. http://www3.interscience.wiley.com/cgi-bin/fulltext/123244296/PDFSTART Google Scholar ›
14
Hand, D. J., and Robert, J. T. (2001), “A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems,” Machine Learning, 45(2), pp. 171-186. Google Scholar ›
15
Hanley, J. A., and Mc Neil, B. J. (1982). “The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology,” 143, pp. 29-36. Google Scholar ›
16
Hong, C. S. (2009), “Optimal Threshold from ROC and CAP Curves,” Communications in Statistics - Simulation and Computation, 38, pp. 2060-2072. Google Scholar ›
17
Hong, H. J., and Cho, S. B. (2009). “Case-Based Reasoning Approaches by Considering Variable Covariance Structure and Variable Weight: Corporate Bankruptcy Prediction,” Korean Management Review, 38(5), pp. 1165-1184. Google Scholar ›
18
Hotoğlu, E., Sen, S., and Can, B. (2025), “A Comprehensive Analysis of Adversarial Attacks against Spam Filters,” ar Xiv preprint ar Xiv: 2505.03831. Google Scholar ›
19
ISEDC, I., Science Economic Development Canada. (2024). Canada's Anti-Spam Legislation (CASL) Enforcement Activities 2023-2024. https://ised-isde.canada.ca/site/canadian-anti-spam-legislation/en Google Scholar ›
20
Kaspersky. (2025). Kaspersky Security Bulletin: Spam and Phishing in 2024. https://securelist.com/spam-and-phishing-in-2024/112345/ Google Scholar ›
21
Keizer, G. (2005). Symantec false positive cripples thousands of Chinese PCs: Virus signature update mistakes critical Windows files for malware. Computerworld. http://www.computerworld Google Scholar ›
22
Krzanowski, W. J., and Hand, J. (2009), ROC curves for continuous data. Chapman & Hall/CRC. Google Scholar ›
23
Kwon, C., and Farrell, P. M. (2000), The Magnitude and Challenge of False-Positive Newborn Screening Test Results. Archives of Pediatrics & Adolescent Medicine, 154(7), pp. 714-718. Google Scholar ›
24
Lee, A. R., and Kwak, C. (2021), “Investigating the Factors Influencing User Churning Behavior in Spam Filtering Apps: A Comparison between Churners and Users and Big Data Analysis of App Logs,” Korean Management Review, 50(1), pp. 197-214. Google Scholar ›
25
Lueg, C. P. (2005), “The Hidden Impacts of Anti-Spam Measures and Their Contribution to the Digital Divide: An Exploratory Study,” in Proceedings of the American Society for Information Science and Technology, 41(1), pp. 176-183. Google Scholar ›
26
Metz, C. E. (1978). “Basic principles of ROC analysis,” Seminar in Nuclear Medicine, 8(4), pp. 283-298. Google Scholar ›
27
Mozer, M., C., Dodier, R., Colagrosso, M. D., Guerra-Salcedo, C., and Wolniewicz, R. (2002). Prodding the ROC Curve: Constrained Optimization of Classifier Performance. Neural Information Processing Systems (NIPS 2002). Google Scholar ›
28
Park, S. C., Lee, W. K., Koh, J., and Ryoo, S. Y. (2020), “Identifying Shadow Work Mechanism in Digital Technology Environments,” Korean Management Review, 49(1), pp. 31-50. Google Scholar ›
29
Pavlov, O., Melville, N., and Plice, R. (2005), “Mitigating the tragedy of the digital commons: The problem of unsolicited commercial e-mail,” Communications of AIS, 2005(16), pp. 73-90. Google Scholar ›
30
Pelletier, L., Almhana, J., and Choulakian, V. (2004). Adaptive Filtering of SPAM. in Proceedings of the Second Annual Conference on Communication Networks and Services Research. Google Scholar ›
31
Roumeliotis, K. I., Tselikas, N. D., and Nasiopoulos, D. K. (2024), “Next-generation spam filtering: Comparative fine-tuning of LLMs, NLPs, and CNN models for email spam classification,” Electronics, 13(11), 2034. Google Scholar ›
32
Rubenking, N. J. (2004). Two Roads to a Spam-Free In-Box. PC Magazine, 23(22), pp. 52-52. Google Scholar ›
33
Sahami, M, Dumais, S., Heckerman, D., and Horvitz, E. (1998). A Bayesian Approach To Filtering Junk Email. AAAI Workshop on Learning for Text Categorization. ftp://ftp.research.microsoft.com/pub/ejh/junkfilter.pdf Google Scholar ›
34
Sahami, M., Dumais, S., Heckerman, D., and Horvitz, E. (1998). A Bayesian Approach To Filtering Junk Email. AAAI Workshop on Learning for Text Categorization. ftp://ftp.research.microsoft.com/pub/ejh/junkfilter.pdf Google Scholar ›
35
Services, A. I. (2024). The latest phishing statistics. https://aag-it.com/the-latest-phishing-statistics/ Google Scholar ›
36
Shavelson, R. J. (1996). Statistical Reasoning for the Behavioral Sciences. 3rd ed. Needham Heights. MA: ALLYN and BACON. Google Scholar ›
37
Spamhaus-Project. (2025). Spamhaus Botnet Threat Update 2024–2025. https://www.spamhaus.org/news/article/861/ Google Scholar ›
38
Srinivasan, A. (1999). Note on the Location of Optimal Classifiers in ROC Space. Oxford University Technical Report (PRG-TR-2-99). ftp://ftp.comlab.ox.ac.uk/pub/Packages/ILP/Papers/AS/roc.ps.gz Google Scholar ›
39
Sun, C. (2008). 8 ways to fight spam filter frustration. Computerworld. http://www.computerworld Google Scholar ›
40
Swets, J. A. (1988). Measuring the Accuracy of Diagnostic Systems. Science, 240(4857), 1285-1293. Google Scholar ›
41
van Erkel, A., R., and Pattynama, P. M. (1998), “Receiver operating characteristic (ROC) analysis: Basic principles and applications in radiology,” European Journal of Radiology, 27(2), pp. 88-94. Google Scholar ›
42
Verizon. (2024). Verizon 2024 Data Breach Investigations Report. https://www.verizon.com/business/resources/TB/2024-data-breach-investigations-report.pdf Google Scholar ›
43
Wang, X. (2025), Spam Filtering in the Modern Era: A Review of Machine Learning, Deep Learning, and System Comparisons. Google Scholar ›
44
Zorkadis, V, Karras, D. A., and Panayotou, M. (2005), “Efficient Information Theoretic Strategies for Classifier Combination, Feature Extraction and Performance Evaluation in Improving False Positives and False Negatives for Spam E-Mail Filtering,” Neural Networks. Google Scholar ›
45
Zorkadis, V., Karras, D. A., and Panayotou, M. (2005), “Efficient Information Theoretic Strategies for Classifier Combination, Feature Extraction and Performance Evaluation in Improving False Positives and False Negatives for Spam E-Mail Filtering. Neural Networks,” 18(5-6), pp. 799-807. Google Scholar ›
46
Zou, K., H., O’Malley, A. J., and Mauri, L. (2007), “Receiver-Operating Characteristic Analysis for Evaluating Diagnostic Tests and Predictive Models,” Circulation, 115, pp. 654-657. Google Scholar ›
47
Zweig, M. H., and Campbell, G. (1993), “Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clinical Chemistry,” 39(4), pp. 561-577. Google Scholar ›

Managing Anti-Spam Filters: Determining the Optimal Level of Filtration for an Organization