Email Spam Filtering: A Systematic Review

By Gordon V. Cormack. Foundations and Trends in Information Retrieval, 2008

346 citations recorded by the source corpus.

Subfields: Information Systems, Information Systems and Management, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/7be97249e88516058f34ad3836c16096413bd02b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

In Information Systems and Management

In Sociology and Political Science

Citations in the corpus

Feature subset selection for logistic regression via mixed integer optimization (2016, background): “binary classification problems are prediction of corporate bankruptcy [3], cancer diagnosis [14], and e-mail spam filtering [11].”
Bayesian mixed-effects inference on classification performance in hierarchical data sets (2012, background): “Another example is spam detection, where a classifier is trained separately for each user to predict content classes from high-dimensional document signatures (Cormack, 2008).”
Enhancing robustness of machine learning systems via data transformations (2017, background): “Machine learning is being used in a number of essential applications such as image recognition [26], natural language processing [9], spam detection [10], autonomous vehicles [8], [33] and even malware detection [46], [11].”
Outlier Analysis (2013, methodology): “A survey of methods for email spam filtering may be found in [67, 124].”
Contributions to the study of SMS spam filtering: new collection and results (2011, methodology): “In addition, we did not perform language-specific preprocessing techniques such as stop word removal or word stemming, since other researchers found that such techniques tend to hurt spam-filtering accuracy [5, 17].”
Towards SMS Spam Filtering: Results under a New Dataset (2013, methodology): “[3], [4], [5], [6], [7], [8], [9], [10], [11]), there are still few studies about SMS spam filtering available in the literature.”
TREC 2007 Spam Track Overview (2007, result): “Other evaluation efforts – and their results – are compared and contrasted with the spam track in a recent survey [ 8 ].”
Machine learning for email spam filtering: review, approaches and open research problems (2019, methodology): “Cormack [20] reviewed previously proposed spam filtering algorithms up to 2008 with specific emphasis on efficiency of the proposed systems.”
Uncovering social spammers: social honeypots + machine learning (2010, background): “Since tweets are extremely short (140 characters or less), we consider a bag-of-words model and a sparse bigrams model [9].”
Dimensionality Reduction as a Defense against Evasion Attacks on Machine Learning Classifiers (2017, neutral): “Machine learning is being used in a number of essential applications such as image recognition [1], natural language processing [2], spam detection [3], autonomous vehicles [4, 5] and even malware detection [6, 7].”