Fuzzy markup language for malware behavioral analysis

Hsien De Huang, Giovanni Acampora, Vincenzo Loia, Chang Shing Lee, Hani Hagras, Mei Hui Wang, Hung-Yu Kao, Jee Gong Chang

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Citations (Scopus)

Abstract

In recent years, antimalware applications represented one of the most important research topics in the area of network security threat. In addition, malware have become a growing important problem for governments and commercial organizations. The key point of the research on the network security is to judge and validate the similarity metrics among the malicious software. Indeed, most computer network issues are also caused by malware. As a consequence, one enhanced system to analyze the behavior of malwares is needed to try to predict the malicious actions and to minimize the computer damages caused by the malware. However, the conventional data analysis tools lack the ability to deal with the computer safety because the environments malwares operating are with high levels of imprecision and vagueness. For this reason, we have developed Taiwan Malware Analysis Net (TWMAN) to improve the accuracy of malware behavioral analysis. This chapter tries to explorer and deal with these computer security and safety issues by integrating the semantic technologies and computational intelligence methods, such as the fuzzy ontologies and fuzzy markup language (FML). With the proposed approach, the malware identification has achieved a good performance according to the experimental results.

Original languageEnglish
Title of host publicationOn the Power of Fuzzy Markup Language
Pages113-132
Number of pages20
Volume296
DOIs
Publication statusPublished - 2013

Publication series

NameStudies in Fuzziness and Soft Computing
Volume296
ISSN (Print)1434-9922

Fingerprint

Markup languages
Malware
Network Security
Network security
Safety
Computer Security
Language
Vagueness
Computational Intelligence
Imprecision
Taiwan
Computer Networks
Security of data
Computer networks
Data analysis
Ontology
Artificial intelligence
Damage
Computer systems
Semantics

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Computational Mathematics

Cite this

Huang, H. D., Acampora, G., Loia, V., Lee, C. S., Hagras, H., Wang, M. H., ... Chang, J. G. (2013). Fuzzy markup language for malware behavioral analysis. In On the Power of Fuzzy Markup Language (Vol. 296, pp. 113-132). (Studies in Fuzziness and Soft Computing; Vol. 296). https://doi.org/10.1007/978-3-642-35488-5-7
Huang, Hsien De ; Acampora, Giovanni ; Loia, Vincenzo ; Lee, Chang Shing ; Hagras, Hani ; Wang, Mei Hui ; Kao, Hung-Yu ; Chang, Jee Gong. / Fuzzy markup language for malware behavioral analysis. On the Power of Fuzzy Markup Language. Vol. 296 2013. pp. 113-132 (Studies in Fuzziness and Soft Computing).
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Huang, HD, Acampora, G, Loia, V, Lee, CS, Hagras, H, Wang, MH, Kao, H-Y & Chang, JG 2013, Fuzzy markup language for malware behavioral analysis. in On the Power of Fuzzy Markup Language. vol. 296, Studies in Fuzziness and Soft Computing, vol. 296, pp. 113-132. https://doi.org/10.1007/978-3-642-35488-5-7

Fuzzy markup language for malware behavioral analysis. / Huang, Hsien De; Acampora, Giovanni; Loia, Vincenzo; Lee, Chang Shing; Hagras, Hani; Wang, Mei Hui; Kao, Hung-Yu; Chang, Jee Gong.

On the Power of Fuzzy Markup Language. Vol. 296 2013. p. 113-132 (Studies in Fuzziness and Soft Computing; Vol. 296).

Research output: Chapter in Book/Report/Conference proceedingChapter

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Huang HD, Acampora G, Loia V, Lee CS, Hagras H, Wang MH et al. Fuzzy markup language for malware behavioral analysis. In On the Power of Fuzzy Markup Language. Vol. 296. 2013. p. 113-132. (Studies in Fuzziness and Soft Computing). https://doi.org/10.1007/978-3-642-35488-5-7