Semantic Set Analysis for Malware Detection

Abstract : Nowadays, malware is growing rapidly through the last few years and becomes more and more sophisticated as well as dangerous. A striking malware is obfuscation malware that is very difficult to detect. This kind of malware can create new variants that are similar to original malware feature but different about code. In order to deal with such types of malware, many approaches have been proposed, however, some of these approaches are ineffective due to their limited detection range, huge overheads or manual stages. Malware detection based on signature, for example, cannot overcome the obfuscation techniques of malware. Likewise, the behavior-based methods have the natural problems of a monitoring system such as recovery costs and long-lasting detection time. In this paper, we propose a new method (semantic set method) to detect metamorphic malware effectively by using semantic set (a set of changed values of registers or variables allocated in memory when a program is executed). For more details, this semantic set is analyzed by n-gram separator and Naïve Bayes classifier to increase detection accuracy and reduce detection time. This system has been already experimented on different datasets and got the accuracy up to 98% and detection rate almost 100%.
Type de document :
Communication dans un congrès
Khalid Saeed; Václav Snášel. 13th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Nov 2014, Ho Chi Minh City, Vietnam. Springer, Lecture Notes in Computer Science, LNCS-8838, pp.688-700, 2014, Computer Information Systems and Industrial Management. 〈10.1007/978-3-662-45237-0_62〉
Liste complète des métadonnées

Littérature citée [6 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01405667
Contributeur : Hal Ifip <>
Soumis le : mercredi 30 novembre 2016 - 11:30:43
Dernière modification le : jeudi 1 décembre 2016 - 01:04:16
Document(s) archivé(s) le : lundi 27 mars 2017 - 08:09:23

Fichier

978-3-662-45237-0_62_Chapter.p...
Fichiers produits par l'(les) auteur(s)

Licence


Distributed under a Creative Commons Paternité 4.0 International License

Identifiants

Citation

Nguyen Nhuong, Vo Nhi, Nguyen Cam, Mai Phu, Cao Tan. Semantic Set Analysis for Malware Detection. Khalid Saeed; Václav Snášel. 13th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Nov 2014, Ho Chi Minh City, Vietnam. Springer, Lecture Notes in Computer Science, LNCS-8838, pp.688-700, 2014, Computer Information Systems and Industrial Management. 〈10.1007/978-3-662-45237-0_62〉. 〈hal-01405667〉

Partager

Métriques

Consultations de la notice

96

Téléchargements de fichiers

86