https://github.com/ozlemkorpe/malware-analysis-with-machine-learning
Project aims to predict if a software is malware or not by using system call sequences in different window sizes.
https://github.com/ozlemkorpe/malware-analysis-with-machine-learning
machine-learning malware malware-analysis malware-detection system-call-analysis
Last synced: 2 months ago
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Project aims to predict if a software is malware or not by using system call sequences in different window sizes.
- Host: GitHub
- URL: https://github.com/ozlemkorpe/malware-analysis-with-machine-learning
- Owner: ozlemkorpe
- Created: 2020-08-10T22:24:23.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-08-10T23:33:31.000Z (over 5 years ago)
- Last Synced: 2025-10-10T11:31:56.384Z (2 months ago)
- Topics: machine-learning, malware, malware-analysis, malware-detection, system-call-analysis
- Language: MATLAB
- Homepage:
- Size: 17.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 2
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Metadata Files:
- Readme: README.md
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README
# Malware-Analysis-with-Machine-Learning
Project aims to predict if a software is malware or not by using system call sequences in different window sizes.
## Basic Usage
* .m files should be opened with MATLAB.
* Datasets are seperated by window sizes, data_5 has window size as 5 etc.
* Last columns in datasets are 1 for the malicious software, 0 for not.
* Datasets should be in the same path with the code, if not path of the datasets must be set manually or fixed in the code.
```
data = readtable('PATH OF THE DATASET');
```
* For plotting the result in bar plot, algorithm names must be uniqe for each row.
## Results
Accuracy results of different algorithms and customizations are stored in Results folder in results.csv.
## Authors
* **Özlem Körpe** - *Initial work* - [ozlemkorpe](https://github.com/ozlemkorpe)