Proceedings of the 2010 ACM Symposium on Applied Computing 2010
DOI: 10.1145/1774088.1774505
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Detecting metamorphic malwares using code graphs

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Cited by 84 publications
(57 citation statements)
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“…Characteristic or behavior of an executable file are the features which can represent the file itself. These features include n-grams [17], [18], instruction sequences (opcodes) [19], [20], API call sequences [21], [13], control flow graph [22], [23], etc.…”
Section: Heuristic-basedmentioning
confidence: 99%
“…Characteristic or behavior of an executable file are the features which can represent the file itself. These features include n-grams [17], [18], instruction sequences (opcodes) [19], [20], API call sequences [21], [13], control flow graph [22], [23], etc.…”
Section: Heuristic-basedmentioning
confidence: 99%
“…For example, Lee [22] create their graph by transforming a Portable Executable (PE) file into a call graph with nodes and edges which represent system calls and system call sequence, respectively. After that, minimization is applied to the call graph turning it into a code graph to speed up the analysis and comparison process.…”
Section: Figure 1 Organization Of Malware Detectionmentioning
confidence: 99%
“…The above studies compare graphs using different graph matching techniques such as formula building using intersection and union of graphs, weighted common behavioural graph generation based on an approximate algorithm [22], and maximal common subgraph [19,25]. However, due to NP-completeness of the problem and the computational complexity inherent in such API call graph matching algorithms [10], they are prohibitively expensive to use for large graphs.…”
Section: Figure 1 Organization Of Malware Detectionmentioning
confidence: 99%
“…The behavior analysis approaches are classified into static approaches [2,5,8] and dynamic approaches [9,10]. The static approaches not limited on the behavior based approaches are light-weight and scalable.…”
Section: Related Workmentioning
confidence: 99%