Tabular Classification

7min

The below input parameters are for different attack types. To start working with the APIs view the Tabular Classification﻿.
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. 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.
﻿Downloa sample model﻿
All files uploaded should be in zipped format. The above files are sample data.
Common parameters
The below table parameters are common for all attact types.
To see the additional parameter specific to attact type refer to the below sections. 
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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normalize_data 
String
Model trained on Normalized data.
if model is trained on normalized data, then set this parameter as "yes" else "no". 
input_dimensions
String
length of dataset * features
the parameter should be string in the format "(height, weight, channel)" For example 100,18
number_of_classes
String
Number of prediction classes.  
the parameter should be string. Example  : Banking Marketing 
Campaign classification dataset 2 (Range >0 & <=200) 
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
If use_model_api is Yes, then provide API details of hosted model as encrypted JSON 
string is mandatory
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. 
model_framework
String
Original model is built with scikit-learn framework. (Option:[scikit learn])
curretly supported framework are: tensorflow, scikit-learn, keras
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 
is_category_columns 
String
If any categorical column presents in dataset provide 'yes' otherwise 
'no'
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categorical_columns_info
String
If is_category_columns is yes, then provide the column names of the categorical. (Provide these columns name as comma separated)
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Parameter
Data type
Descrption
Remark
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
number_of_attack_queries
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String
Number of attack sample to use for analysis. 
Generally heigher the number of attack queries , better would be the analysis. Accepted range: (0-400000)
vulnerability_threshold
String 
Stolen model accuracy threshold at which defense model should get generated. 
Parameter value Range: (0.0 - 1.0).
Note: use Common parameters﻿  only, no extra parameters is needed. 
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To improve the accuracy, you can experiment with the following values for your attack input parameters. In our example we have used an BFSI dataset in our model and the below table reflects the parameters suitable for it. For more information, please refer to the reference implementation.
Task Pair/Analysis Type
Model under analysis
Type of Attack Strategy 
No of queries 
Outcome
TC - MEA
BFSI 
Blackbox
10000 
Stolen model accuracy between 75%-85%
TC - MEA
BFSI 
Greybox
20000 
Stolen model accuracy between 80%-90%
TC - EVA 
BFSI
N/A
N/A
Evasion Report
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 Tabular classification, supported attack types are - Extraction
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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)
normalize_data	String	Model trained on Normalized data.	if model is trained on normalized data, then set this parameter as "yes" else "no".
input_dimensions	String	length of dataset * features	the parameter should be string in the format "(height, weight, channel)" For example 100,18
number_of_classes	String	Number of prediction classes.	the parameter should be string. Example : Banking Marketing Campaign classification dataset 2 (Range >0 & <=200)
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	If use_model_api is Yes, then provide API details of hosted model as encrypted JSON string is mandatory	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.
model_framework	String	Original model is built with scikit-learn framework. (Option:[scikit learn])	curretly supported framework are: tensorflow, scikit-learn, keras
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
is_category_columns	String	If any categorical column presents in dataset provide 'yes' otherwise 'no'
categorical_columns_info	String	If is_category_columns is yes, then provide the column names of the categorical. (Provide these columns name as comma separated)

Parameter	Data type	Descrption	Remark
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
number_of_attack_queries	String	Number of attack sample to use for analysis.	Generally heigher the number of attack queries , better would be the analysis. Accepted range: (0-400000)
vulnerability_threshold	String	Stolen model accuracy threshold at which defense model should get generated.	Parameter value Range: (0.0 - 1.0).

Task Pair/Analysis Type	Model under analysis	Type of Attack Strategy	No of queries	Outcome
TC - MEA	BFSI	Blackbox	10000	Stolen model accuracy between 75%-85%
TC - MEA	BFSI	Greybox	20000	Stolen model accuracy between 80%-90%
TC - EVA	BFSI	N/A	N/A	Evasion Report

Updated 31 Jan 2024

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Tabular

Time Series

Tabular Classification

File upload format

Common parameters

Extraction parameters

Evasion parameters

Experimentation with values