Time Series Forecasting

4min

The below input parameters are for different attack types. To start working with the APIs view the Times Series Forecasting﻿.
File upload format
Data: Data should be in a CSV file with a header as all the features (Columns) name and the last column as the target variable.
﻿Downloa sample data﻿
Minmax: Data should be in a CSV file with a header as all the feature (Columns) names and the last column as the target variable. The first row of the CSV file should contain the minimum value for each column (feature), and the second row should contain the max value. 
﻿Downloa sample minmax﻿
Model: The model should be saved in either .pkl, .h5 or TensorFlow format with full architecture. Full architecture is needed when loading the model to the platofrm for assessment either in encrypted or unencrypted. This can be ignored when model is hosted as an API.
﻿Download sample model﻿
All files uploaded should be in zipped format. The above files are sample data.
Parameter
Data type
Descrption
Remark
model_id  
String
Model_id received during model registration. We need to provide 
this model_id  in query parameter in URL.
you have to do model registration only once for a model and you can perform many analysis. This will help you to track how many api call has been made, how many has successed.
Request Body (Json format)
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attack_type
String
You can select the attack type either Blackbox or Greybox. 
Blackbox: for performing model analysis, no information about model or data will be used.                                              Greybox: information about data will be leverage for creation of attack data
Note: only 2-5 % of data is needed
input_dimensions
String
Input dimension (Example 100,6 for Pump Sensor)
the parameter should be string. 
number_of_attack_queries
String
Number of attack queries that model will be subjected to.
generally Heigher the number of attack queries, better would be the analysis. And it would take more time to process. (Range:  >0 
& <=400000)
model_framework 
String
Original model is built with tensorflow framework.(Option :[tensorflow])
curretly supported framework are: tensorflow, scikit-learn, keras
model_api_details
String
If use_model_api is Yes, then provide API details of hosted model as 
encrypted JSON string is mandatory
provide this only if use_model_api is "yes". 
use_model_api
String
Use model API to train your model instead of uploading the model as a zip.
when this parameter is yes, you don't have to upload model as zip. You can pass api url along with other verification credential in json file. 
defense_bestonly
String
Highly optimized defense model will be returned.
when selected "yes", it will train N number of model and select best model. Ofcourse this will take longer time. If "no", then once defense model accuracy reached above 95% It will stop 
encryption_strategy
Int
Choose a encryption strategy for you model. if model is uploaded directly as a 
zip pick 0, 1 if model is encryted as .pyc and 
uploaded as a zip. Ignore if use_model_api is 
Yes
select 0: pass tensorflow model as it is, select 1: pass encrypted model. It could be .pyc file 
vulnerability_threshold 
String
Threshold percent of stolen model accuracy at which defense model should be generated 
parameter value will be between 0 to 1.
To access all sample artifacts, please visit Artifacts﻿. 
For specific artifact details,  refer 
Vulnerability Report : Vulnerability Report﻿     
Sample Attacks : Sample Attacks﻿﻿
Defense Report: Defense Report﻿                    
Defense Model: Defense Model﻿﻿
Note: For Time series forecasting, supported attack types are - Extraction
﻿

Parameter	Data type	Descrption	Remark
model_id	String	Model_id received during model registration. We need to provide this model_id in query parameter in URL.	you have to do model registration only once for a model and you can perform many analysis. This will help you to track how many api call has been made, how many has successed.
Request Body (Json format)
attack_type	String	You can select the attack type either Blackbox or Greybox.	Blackbox: for performing model analysis, no information about model or data will be used. Greybox: information about data will be leverage for creation of attack data Note: only 2-5 % of data is needed
input_dimensions	String	Input dimension (Example 100,6 for Pump Sensor)	the parameter should be string.
number_of_attack_queries	String	Number of attack queries that model will be subjected to.	generally Heigher the number of attack queries, better would be the analysis. And it would take more time to process. (Range: >0 & <=400000)
model_framework	String	Original model is built with tensorflow framework.(Option :[tensorflow])	curretly supported framework are: tensorflow, scikit-learn, keras
model_api_details	String	If use_model_api is Yes, then provide API details of hosted model as encrypted JSON string is mandatory	provide this only if use_model_api is "yes".
use_model_api	String	Use model API to train your model instead of uploading the model as a zip.	when this parameter is yes, you don't have to upload model as zip. You can pass api url along with other verification credential in json file.
defense_bestonly	String	Highly optimized defense model will be returned.	when selected "yes", it will train N number of model and select best model. Ofcourse this will take longer time. If "no", then once defense model accuracy reached above 95% It will stop
encryption_strategy	Int	Choose a encryption strategy for you model. if model is uploaded directly as a zip pick 0, 1 if model is encryted as .pyc and uploaded as a zip. Ignore if use_model_api is Yes	select 0: pass tensorflow model as it is, select 1: pass encrypted model. It could be .pyc file
vulnerability_threshold	String	Threshold percent of stolen model accuracy at which defense model should be generated	parameter value will be between 0 to 1.

Updated 21 Mar 2024

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Time Series

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Time Series Forecasting

File upload format

Extraction parameters