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== Overview ==
== Overview ==


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Nevertheless, the two models are highlighting the most abnormal IP addresses (and potentially related users). These lists of addresses could be used as input for further investigation by an operational expert.
Nevertheless, the two models are highlighting the most abnormal IP addresses (and potentially related users). These lists of addresses could be used as input for further investigation by an operational expert.


**Authors**: Nicolas Stucki & Thomas Levy
* Authors : Nicolas Stucki & Thomas Levy
 
* Keywords: Unsupervised detection, clustering, outlier detection, Isolation Forest, interquartile range (IQR)
**Keywords**: Unsupervised detection, clustering, outlier detection, Isolation Forest, interquartile range (IQR)  


== Data==
== Data==
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This use case relies on the dataset ["Online Shopping Store - Web Server Logs"](https://doi.org/10.7910/DVN/3QBYB5).
This use case relies on the dataset ["Online Shopping Store - Web Server Logs"](https://doi.org/10.7910/DVN/3QBYB5).


The dataset has been processed to convert it from a raw log file format into tabular elements corresponding to client requests to the web site.
The dataset has been processed to convert it from a raw log file format into tabular elements corresponding to client requests to the web site. Then, to reduce its size, it has been sampled to keep only requests coming from a sub-part of the client IP adresses. Finally, 3 csv files have been ingested into the platform:
Then, to reduce its size, it has been sampled to keep only requests coming from a sub-part of the client IP adresses.
 
Finally, 3 csv files have been ingested into the platform:
* Small size dataset: logs_sub_2.csv (211 335 lines)
- Small size dataset: logs_sub_2.csv (211 335 lines)
* Medium size dataset: logs_sub_5.csv (495 997 lines)
- Medium size dataset: logs_sub_5.csv (495 997 lines)
* Large size dataset: logs_sub_10.csv (1 030 453 lines)
- Large size dataset: logs_sub_10.csv (1 030 453 lines)


Main columns used are:
Main columns used are:
 
{| class="wikitable"
| Name | Description |
|+
|:--- | :---|
!Name
| SourceIP | Source IP address |
!Description
| datetime | Local date&time of the reception of the request by the web server  |
!
| method | HTTP method of the request (i.e GET, POST..) |
|-
|SourceIP
|Source IP address   
|
|-
|datetime
|Local date&time of the reception of the request by the web server   
|
|-
|method
|HTTP method of the request (i.e GET, POST..)
|
|}


===  Specific feature engineering===  
===  Specific feature engineering===  
Not applicable
Not applicable


===  Notebooks===  
===  Notebooks===
 
{| class="wikitable"
| Notebook | Data Science step                                |
|+
|:--- |:---:|
!Notebook
| UseCase7_DataPrep.ipynb         | *Data preparation (executed outside the platform)* |
!Data Science step     
| UseCase7_Detection.ipynb   | *Security events detection (including simple feature engineering)* |
!
 
|-
===  Risks & Compliance===
|UseCase7_DataPrep.ipynb  
 
|''Data preparation (executed outside the platform)''
| Type | Applicable | Comment(s) (if applicable) |
|
|:--- |:---:|:---|
|-
| Bias | No | Has a complete bias study been carried out ? Comments if applicable |
|UseCase7_Detection.ipynb  
| Ethics committee | No | Does the project needs to be screened by an ethics committee and if so has this been achieved ? Comments if applicable |
|''Security events detection (including simple feature engineering)''
| RSSI | No | Does the project needs to inform the RSSI about its outcome and activity, and if so has this been achieved ? Comments if applicable |
|
| DPO | No | Does the project needs to inform the DPO about its outcome and activity, and if so has this been achieved ? Comments if applicable |
|}
| RGPD | No | Are all the data collected necessary to perform the project objective and is there a necessity to collect consent from individuals ? Comments if applicable |
| CNIL | No | Does the project needs to inform the CNIL ? Comments if applicable |
 


===  Requirements===  
===  Risks & Compliance===
{| class="wikitable"
|+
!Type
!Applicable
!Comment(s) (if applicable)     
|-
|Bias
|No
|Has a complete bias study been carried out ? Comments if applicable   
|-
|Ethics committee
|No
|Does the project needs to be screened by an ethics committee and if so has this been achieved ? Comments if applicable
|-
|RSSI
|No
|Does the project needs to inform the RSSI about its outcome and activity, and if so has this been achieved ? Comments if applicable   
|-
|DPO
|No
|Does the project needs to inform the DPO about its outcome and activity, and if so has this been achieved ? Comments if applicable   
|-
|RGPD
|No
|Are all the data collected necessary to perform the project objective and is there a necessity to collect consent from individuals ? Comments if applicable   
|-
|CNIL
|No
|Does the project needs to inform the CNIL ? Comments if applicable
|}


- Python (3.6 or +)
===  Requirements===
- scikit-learn (1.0.2 or +)
- seaborn (0.11.2 or +)


---
* Python (3.6 or +)
* scikit-learn (1.0.2 or +)
* seaborn (0.11.2 or +)


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== Notebooks ==
Retrouvez tous les éléments du Use Case sur le GitLab du Campus Cyber : https://gitlab.com/campuscyber/gt-ia-et-cyber/-/tree/main/UC7%20Suspicious%20security%20events%20detection?ref_type=heads
{{PageSubHeader Commun
{{PageSubHeader Commun
|WorkGroup=IA et cybersécurité
|WorkGroup=IA et cybersécurité
}}
}}

Dernière version du 12 novembre 2024 à 15:38

Développement du Use Case pédagogique "Suspicious security events detection" dans le cadre du GT IA et Cyber

Catégorie : Commun Statut : Production 1 : Idée - 2 : Prototype - 3 : Validation - 4 : Production



Overview[modifier | modifier le wikicode]

Project Objectives[modifier | modifier le wikicode]

The overall objective of the Cyber project is to propose pedagogical notebooks presenting a set of algorithms on a precise cyber application.

Business Objectives[modifier | modifier le wikicode]

The use case (UC7) aims at detecting abnormal security events in web events.

Data & methodologies[modifier | modifier le wikicode]

By exploring web activities, it uses two stastistical methods (interquartile range - IQR - and Isolation Forest) to identify "outlier" behaviors that are rare compared to the most standard behaviors.

Results[modifier | modifier le wikicode]

This section is not really applicable since the current objective is not targeted towards production or POC. Nevertheless, the two models are highlighting the most abnormal IP addresses (and potentially related users). These lists of addresses could be used as input for further investigation by an operational expert.

  • Authors : Nicolas Stucki & Thomas Levy
  • Keywords: Unsupervised detection, clustering, outlier detection, Isolation Forest, interquartile range (IQR)

Data[modifier | modifier le wikicode]

Online Shopping Store[modifier | modifier le wikicode]

This use case relies on the dataset ["Online Shopping Store - Web Server Logs"](https://doi.org/10.7910/DVN/3QBYB5).

The dataset has been processed to convert it from a raw log file format into tabular elements corresponding to client requests to the web site. Then, to reduce its size, it has been sampled to keep only requests coming from a sub-part of the client IP adresses. Finally, 3 csv files have been ingested into the platform:

  • Small size dataset: logs_sub_2.csv (211 335 lines)
  • Medium size dataset: logs_sub_5.csv (495 997 lines)
  • Large size dataset: logs_sub_10.csv (1 030 453 lines)

Main columns used are:

Name Description
SourceIP Source IP address   
datetime Local date&time of the reception of the request by the web server   
method HTTP method of the request (i.e GET, POST..)

Specific feature engineering[modifier | modifier le wikicode]

Not applicable

Notebooks[modifier | modifier le wikicode]

Notebook Data Science step     
UseCase7_DataPrep.ipynb Data preparation (executed outside the platform)
UseCase7_Detection.ipynb   Security events detection (including simple feature engineering)

Risks & Compliance[modifier | modifier le wikicode]

Type Applicable Comment(s) (if applicable)     
Bias No Has a complete bias study been carried out ? Comments if applicable   
Ethics committee No Does the project needs to be screened by an ethics committee and if so has this been achieved ? Comments if applicable
RSSI No Does the project needs to inform the RSSI about its outcome and activity, and if so has this been achieved ? Comments if applicable   
DPO No Does the project needs to inform the DPO about its outcome and activity, and if so has this been achieved ? Comments if applicable   
RGPD No Are all the data collected necessary to perform the project objective and is there a necessity to collect consent from individuals ? Comments if applicable   
CNIL No Does the project needs to inform the CNIL ? Comments if applicable

Requirements[modifier | modifier le wikicode]

  • Python (3.6 or +)
  • scikit-learn (1.0.2 or +)
  • seaborn (0.11.2 or +)

Notebooks[modifier | modifier le wikicode]

Retrouvez tous les éléments du Use Case sur le GitLab du Campus Cyber : https://gitlab.com/campuscyber/gt-ia-et-cyber/-/tree/main/UC7%20Suspicious%20security%20events%20detection?ref_type=heads

Groupe de travail

IA et cybersécurité