Published February 29, 2020 | Version v1
Journal article Open

Predicting the Dynamic Behaviour of Malware using RNN

  • 1. Department of Information Technology, SRM Institute of Science and Technology, Chennai, India.
  • 1. Publisher

Description

Malware analysis can be classified as static and dynamic analysis. Static analysis involves the inspection of the malicious code by observing the features such as file signatures, strings etc. The code obfuscation techniques such as string encryption, class encryption etc can be easily applied on static code analysis. Dynamic or behavioural data is more difficult to obfuscate as the malicious payload may have already been executed before it is detected. In this paper, the dataset is obtained from repositories such as VirusShare and is run in Cuckoo Sandbox with the help of the agent.py. The dynamic features are extracted from the generated Cuckoo logs in the html and JSON format and it has to be determined whether it is malicious or not using recurrent neural networks. Recurrent Neural Networks are capable of predicting whether an executable is malicious and have the ability to capture time-series data.

Files

C6291029320.pdf

Files (788.5 kB)

Name Size Download all
md5:188bf46e56da6f8ff93680bc79246efb
788.5 kB Preview Download

Additional details

Related works

Is cited by
Journal article: 2249-8958 (ISSN)

Subjects

ISSN
2249-8958
Retrieval Number
C6291029320/2020┬ęBEIESP