Detection of unknown malicious script code using a conceptual graph and SVM

Authors:
Hayoung Kim;Junho Choi;Dongjin Choi;Hansuk Choi;Pankoo Kim
Affiliations:
Chosun University, Gwangju, South Korea;Chosun University, Gwangju, South Korea;Chosun University, Gwangju, South Korea;Mokpo National University, Mokpo, South Korea;Chosun University, Gwangju, South Korea
Venue:
Proceedings of the 2012 ACM Research in Applied Computation Symposium
Year:
2012

Citing 5
Cited 0

Learning to Generate CGs from Domain Specific Sentences

ICCS '01 Proceedings of the 9th International Conference on Conceptual Structures: Broadening the Base
Conceptual Graph Matching for Semantic Search

ICCS '02 Proceedings of the 10th International Conference on Conceptual Structures: Integration and Interfaces
Static analysis of executables to detect malicious patterns

SSYM'03 Proceedings of the 12th conference on USENIX Security Symposium - Volume 12
Construction of conceptual graph representation of texts

HLT-SRWS '04 Proceedings of the Student Research Workshop at HLT-NAACL 2004
Efficient Malicious Code Detection Using N-Gram Analysis and SVM

NBIS '11 Proceedings of the 2011 14th International Conference on Network-Based Information Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

There are a lot of malicious codes on the internet and many research studies methods for detection of them. Generally, detection methods of malicious codes compare source codes through definition and analysis pattern of malicious codes. In this paper, proposed method is a malicious code detection using relations and concepts between codes pattern based on semantics. Also, this method is detection of malicious script code through token conceptualization for extraction of relations and concepts in source codes because conceptual graph and regularization pattern matching between malicious behaviors in codes. In experiment, we test a malicious behavior distinction based on SVM(Support Vector Machine) training and the result is indicated adequate rate of malicious code detection.