Earlier works on detecting spam e-mails usually compare the contents of e-mails against specific keywords, which are not robust as the spammers frequently change the terms used in e-mails. We have presented in this paper a novel featuring method for spam filtering. Instead of classifying e-mails according to keywords, this study analyzes the spamming behaviors and extracts the representative ones as features for describing the characteristics of e-mails. An back-propagation neural network is designed and implemented, which builds classification model by considering the behavior-based features revealed from e-mails' headers and syslogs. Since spamming behaviors are infrequently changed, compared with the change frequency of keywords used in spams, behavior-based features are more robust with respect to the change of time; so that the behavior-based filtering mechanism outperform keyword-based filtering. The experimental results indicate that our methods are more useful in distinguishing spam e-mails than that of keywordbased comparison.

This paper presents a two-stage genetic mechanism for the migration-based load balance of virtual machine hosts (VMHs) in cloud computing. Previous methods usually assume this issue as a jobassignment optimization problem and only consider the current VMHs' loads; however, without considering loads of VMHs after balancing, these methods can only gain limited effectiveness in real environments. In this study, two genetic-based methods are integrated and presented. First, performance models of virtual machines (VMs) are extracted from their creating parameters and corresponding performance measured in a cloud computing environment. The gene expression programming (GEP) is applied for generating symbolic regression models that describe the performance of VMs and are used for predicting loads of VMHs after load-balance. Secondly, with the VMH loads estimated by GEP, the genetic algorithm considers the current and the future loads of VMHs and decides an optimal VM-VMH assignment for migrating VMs and performing load-balance. The performance of the proposed method is evaluated in a real cloudcomputing environment, Jnet, wherein the aforementioned methods are implemented as a centralized load balancing mechanism. The experimental results show that our method outperforms previous methods, such as heuristics and statistics regression.

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