Using new scheduling heuristics based on resource consumption information for increasing throughput on rule-based spam filtering systems

Abstract

Summary The large increase of spam deliveries since the first half of 2013 entailed hard to solve troubles in spam filters. In order to adequately fight spam, the throughput of spam filtering platforms should be necessarily increased. In this context, and taking into consideration the widespread utilization of rule‐based filtering frameworks in the spam filtering domain, this work proposes three novel scheduling strategies for optimizing the time needed to classify new incoming e‐mails through an intelligent management of computational resources depending on the Central Processing Unit (CPU) usage and Input/Output (I/O) delays. In order to demonstrate the suitability of our approaches, we include in our experiments a comparative study in contrast to other successful heuristics previously published in the scientific literature. Results achieved demonstrated that one of our alternative heuristics allows time savings of up to 10% in message filtering, while keeping the same classification accuracy. Copyright © 2015 John Wiley & Sons, Ltd.

Boosting Accuracy of Classical Machine Learning Antispam Classifiers in Real Scenarios by Applying Rough Set Theory

“…Taking this situation into account, it is easy to realize that spam deliveries remain a problem to be solved in the modern society. To cope with this situation, the software industry (headed by Internet security enterprises) has been continuously improving existing antispam filtering techniques and systems in order to enhance both filtering throughput [5][6][7] and classification accuracy.…”

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

“…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.…”

“…To this end, authors in [10] proposed two scheduling strategies (RBM and CEM) for optimizing the time needed to classify new incoming e-mails through an intelligent management of computational resources. To accomplish this task, both strategies use the information about CPU usage and I/O delay of each rule to achieve an adequate execution balance by combining rules that execute I/O operations together with those using intensively CPU resources (perform complex computations).…”

“…To accomplish this task, both strategies use the information about CPU usage and I/O delay of each rule to achieve an adequate execution balance by combining rules that execute I/O operations together with those using intensively CPU resources (perform complex computations). Additionally, in order to increase even more the performance of the previous scheduling strategies, authors in [10] proposed a novel heuristic ensemble method (also called MHE) able to hybridise two individual heuristics to make the most of them. The main difference with previous approaches is that MHE is a heuristic combinatorial scheme instead of a simple heuristic.…”

“…To take more advantage of the above mentioned advances (i.e. achieve the SFE conditions to stop a filter evaluation early and achieve an adequate parallelization of rules), some scheduling heuristics have been introduced [8,9,10].…”