Optimising anti-spam filters with evolutionary algorithms

Yevseyeva, Iryna; Basto‐Fernandes, Vitor; Ruano-Ordás, David; Méndez, J.

doi:10.1016/j.eswa.2013.01.008

Cited by 29 publications

(17 citation statements)

References 15 publications

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“…In previous work on anti-SPAM filter optimization [46] it was observed that many rules were not participating in the classification process and some (with very small weights) only marginally influenced the classification results. This observation suggests that in addition to optimizing fpr and fnr, the complexity of the anti-SPAM filter or its parsimony can be optimized.…”

Section: A Spam Multiobjective Problem Formulationmentioning

confidence: 99%

“…SPAM filtering problem optimization has been addressed by the techniques surveyed in [45], [46]. The formulation of the scores setting optimization problem is naturally bi-objective, a typical user would wish to minimize both, the number of SPAM messages not identified by anti-spam filtering techniques, called false negative rate (fnr), and the number of legitimate messages classified as SPAM by mistake, called false positive rate (fpr).…”

Section: A Spam Multiobjective Problem Formulationmentioning

confidence: 99%

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Multiobjective optimization of classifiers by means of 3D convex-hull-based evolutionary algorithms

Zhao

Basto‐Fernandes

Jiao

et al. 2016

Information Sciences

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Finding a good classifier is a multiobjective optimization problem with different error rates and the costs to be minimized. The receiver operating characteristic is widely used in the machine learning community to analyze the performance of parametric classifiers or sets of Pareto optimal classifiers. In order to directly compare two sets of classifiers the area (or volume) under the convex hull can be used as a scalar indicator for the performance of a set of classifiers in receiver operating characteristic space.Recently, the convex hull based multiobjective genetic programming algorithm was proposed and successfully applied to maximize the convex hull area for binary classification problems. The contribution of this paper is to extend this algorithm for dealing with higher dimensional problem formulations. In particular, we discuss problems where parsimony (or classifier complexity) is stated as a third objective and multi-class classification with three different true classification rates to be maximized.The design of the algorithm proposed in this paper is inspired by indicator-based evolutionary algorithms, where first a performance indicator for a solution set is established and then a selection operator is designed that complies with the performance indicator. In this case, the performance indicator will be the volume under the convex hull. The algorithm is tested and analyzed in a proof of concept study on different benchmarks that are designed for measuring its capability to capture relevant parts of a convex hull.Further benchmark and application studies on email classification and feature selection round up the analysis and assess robustness and usefulness of the new algorithm in real world settings.

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Section: A Spam Multiobjective Problem Formulationmentioning

confidence: 99%

Section: A Spam Multiobjective Problem Formulationmentioning

confidence: 99%

Multiobjective optimization of classifiers by means of 3D convex-hull-based evolutionary algorithms

Zhao

Basto‐Fernandes

Jiao

et al. 2016

Information Sciences

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, we also believe that our WSF2 framework can take advantage from URI Blacklists (URIBL), commonly used in the e-mail filtering domain. In addition to the obvious technical development, we believe that the use of different filter optimization heuristics (e.g., tuning up rule scores, finding and removing irrelevant features, or detecting counterproductive rules) would be very appropriate to complement the current state of the art [51][52][53][54]. Finally, the lack of effective tools for web spam dataset management and maintenance also suggests an interesting option for future research activities.…”

Section: Discussionmentioning

confidence: 99%

WSF2: A Novel Framework for Filtering Web Spam

Fdez-Glez

Ruano-Ordás

Laza

et al. 2016

Scientific Programming

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Over the last years, research on web spam filtering has gained interest from both academia and industry. In this context, although there are a good number of successful antispam techniques available (i.e., content-based, link-based, and hiding), an adequate combination of different algorithms supported by an advanced web spam filtering platform would offer more promising results. To this end, we propose the WSF2 framework, a new platform particularly suitable for filtering spam content on web pages. Currently, our framework allows the easy combination of different filtering techniques including, but not limited to, regular expressions and well-known classifiers (i.e., Naïve Bayes, Support Vector Machines, and C5.0). Applying our WSF2 framework over the publicly available WEBSPAM-UK2007 corpus, we have been able to demonstrate that a simple combination of different techniques is able to improve the accuracy of single classifiers on web spam detection. As a result, we conclude that the proposed filtering platform is a powerful tool for boosting applied research in this area.

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“…In this context, and given the extensive utilization and increasing significance of rule-based filtering frameworks for the anti-spam domain, several studies have addressed the optimization of parameters (rule scores and scheduling plan) to improve their accuracy [4,5,6,7] and classification throughput [8,9,10]. However, previous works on throughput optimization are based on simple heuristics without taking into account its relation to accuracy.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Multi-objective Scheduling for Anti-Spam Filtering Throughput Optimization

Ruano-Ordás

Basto‐Fernandes

Yevseyeva

et al. 2017

Lecture Notes in Computer Science

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Abstract. This paper presents an evolutionary multi-objective optimization problem formulation for the anti-spam filtering problem, addressing both the classification quality criteria (False Positive and False Negative error rates) and email messages classification time (minimization). This approach is compared to single objective problem formulations found in the literature, and its advantages for decision support and flexible/adaptive anti-spam filtering configuration is demonstrated. A study is performed using the Wirebrush4SPAM framework anti-spam filtering and the SpamAssassin email dataset. The NSGA-II evolutionary multi-objective optimization algorithm was applied for the purpose of validating and demonstrating the adoption of this novel approach to the anti-spam filtering optimization problem, formulated from the multi-objective optimization perspective. The results obtained from the experiments demonstrated that this optimization strategy allows the decision maker (anti-spam filtering system administrator) to select among a set of optimal and flexible filter configuration alternatives with respect to classification quality and classification efficiency.

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Optimising anti-spam filters with evolutionary algorithms

Cited by 29 publications

References 15 publications

Multiobjective optimization of classifiers by means of 3D convex-hull-based evolutionary algorithms

Multiobjective optimization of classifiers by means of 3D convex-hull-based evolutionary algorithms

WSF2: A Novel Framework for Filtering Web Spam

Evolutionary Multi-objective Scheduling for Anti-Spam Filtering Throughput Optimization

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