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STATIC AND DYNAMIC MALWARE ANALYSIS USING MACHINE LEARNING

STATIC AND DYNAMIC MALWARE ANALYSIS USING MACHINE LEARNING

Abstract:

Malware detection is an indispensable factor in security of internet oriented machines. The combinations of different features are used for dynamic malware analysis. The different combinations are generated from APIs, Summary Information, DLLs and Registry Keys Changed. Cuckoo sandbox is used for dynamic malware analysis, which is customizable, and provide good accuracy. More than 2300 features are extracted from dynamic analysis of malware and 92 features are extracted statically from binary malware using PEFILE. Static features are extracted from 39000 malicious binaries and 10000 benign files. Dynamically 800 benign files and 2200 malware files are analyzed in Cuckoo Sandbox and 2300 features are extracted. The accuracy of dynamic malware analysis is 94.64% while static analysis accuracy is 99.36%. The dynamic malware analysis is not effective due to tricky and intelligent behaviours of malwares. The dynamic analysis has some limitations due to controlled network behavior and it cannot be analyzed completely due to limited access of network.

SOFTWARE AND HARDWARE REQUIREMENTS:

HARDWARE SPECIFICATIONS:

Processor: I3/Intel

Processor RAM: 4GB (min)

Hard Disk: 128 GB

Key Board: Standard Windows Keyboard

Mouse: Two or Three Button Mouse

Monitor: Any

IDE: PyCharm

Libraries Used: Pandas, Numpy,Flask

SOFTWARE SPECIFICATIONS:

Operating System: Windows 7+

Server-side Script: Python 3.6+

March 17, 2022

1 responses on "STATIC AND DYNAMIC MALWARE ANALYSIS USING MACHINE LEARNING"

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