Name
Abstract | ||
|---|---|---|
Malware is a malicious code which intends to harm computers and networks. Each year, a huge number of malicious programs are released. Therefore, detecting malware has become one of the most important challenges for the security of computer systems. Various methods have been defined for detecting and classifying malware, such as signature-based and heuristic-based techniques. This paper proposes a new malware detection method based on the operational codes (OpCodes) within an executable file by using the evolutionary algorithm. There are several steps in the proposed method, which includes disassembling the executable files, generating a graph of OpCodes and using the evolutionary algorithm to find the most similar graph to each suspicious instance. Finally, the label of each suspicious instance is detected based on the most similar graph obtained from the evolutionary algorithm with each class (family of malware and benign). The results show that, the proposed method can be used as a method for malware detection and malware category.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3319619.3326811 | GECCO |
Keywords | Field | DocType |
Classification, Evolutionary algorithm, Malware detection, OpCode, Graph | Evolutionary algorithm,Computer science,Artificial intelligence,Malware,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-6748-6 | 1 | 0.35 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Farnoush Manavi | 1 | 2 | 2.10 |
| Ali Hamzeh | 2 | 214 | 29.47 |