M4 - AUTOMATED FORENSICABILITY FOR SOFTWARE

Description

Integrate forensics into software source code. 

Courses Where This Module Is Integrated

Activities 

Pre-lab Content Dissemination 

We first need to know what to log and how to log. Let us use the following heuristics. 

In-class Hands-on Experience


Post Lab Experience

Imagine this scenario: Dolly, a data science specialist at Auburn University. University has written machine learning (ML) code in `workshop9.py`. The output is correct, but she just learned about adversarial machine learning, which discusses how ML code can be attacked. She realized that ML models are susceptible to security issues:  

Assist Dolly by writing logging code in the correct locations in `workshop8.py` with comments so that the two issues, as mentioned earlier, are logged for all provided ML functions. After writing the code, put in comments to justify your code, save `workshop9.py`. 

Sample Input/Output:

Input: workshop8.py

Expected Output: Code and Comments

iris = datasets.load_iris()

'''

Inserted logging statement because data can be poisoned.

'''

simpleLogger.info('Generic information: getting results: %s', str(res))