The nature of statistical learning theory
The nature of statistical learning theory
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
ACM Transactions on Information and System Security (TISSEC)
Evaluating cost-sensitive Unsolicited Bulk Email categorization
Proceedings of the 2002 ACM symposium on Applied computing
A Memory-Based Approach to Anti-Spam Filtering for Mailing Lists
Information Retrieval
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Pricing via Processing or Combatting Junk Mail
CRYPTO '92 Proceedings of the 12th Annual International Cryptology Conference on Advances in Cryptology
An Assessment of Case-Based Reasoning for Spam Filtering
Artificial Intelligence Review
Catching spam before it arrives: domain specific dynamic blacklists
ACSW Frontiers '06 Proceedings of the 2006 Australasian workshops on Grid computing and e-research - Volume 54
An HMM for detecting spam mail
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
An empirical study of three machine learning methods for spam filtering
Knowledge-Based Systems
SpamHunting: An instance-based reasoning system for spam labelling and filtering
Decision Support Systems
Spam Filtering Using Statistical Data Compression Models
The Journal of Machine Learning Research
Relaxed online SVMs for spam filtering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Review: A review of machine learning approaches to Spam filtering
Expert Systems with Applications: An International Journal
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
An anti-spam scheme using pre-challenges
Computer Communications
Classification methods in the detection of new malicious emails
Information Sciences: an International Journal
A study of spam filtering using support vector machines
Artificial Intelligence Review
A scalable intelligent non-content-based spam-filtering framework
Expert Systems with Applications: An International Journal
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
Hi-index | 0.07 |
'Spammers exploit the popularity and low cost of e-mail services to send unsolicited messages (spam), which fill users' accounts and waste valuable resources. To combat this problem, many different spam filtering techniques have been proposed in the literature. Nevertheless, most current anti-spamming filtering schemes are based on detecting relevant terms or tokens in the entire message or in only the body, which implies an invasion of users' privacy. In this paper, a novel spam-filtering technique based solely on the information present in headers is introduced. In this approach, headers are considered as the result of a dynamic process that generates characters. The observed characters are treated as signals and parameterised in accordance with standard signal pre-processing techniques by extracting relevant parameters from the header. From this, Hidden Markov Models (HMMs) are considered for a spam detection system. The performance achieved by our proposal is evaluated and compared with that of other pattern classification paradigms used for spam filtering. The experimental results for SpamAssassin, TREC05 and CEAS 2008 Lab Evaluation improve on those results obtained with other widely used techniques, achieving up to 98.42% of spam detection while keeping the false positive rate below 0.4% and with the added advantages of using only information from the headers and being independent of the language in which the e-mail is written.