Voir la source de UC7 : Suspicious security events detection

{{Commun
|ShortDescription FR=Développement du Use Case pédagogique "Suspicious security events detection" dans le cadre du GT IA et Cyber
|ImageStr=Use case-GTIA.png
|Status=Production
}}

== Overview ==

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

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

===  Data & methodologies=== 
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=== 
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==
===  Online Shopping Store=== 
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:
{| class="wikitable"
|+
!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=== 
Not applicable

===  Notebooks===
{| class="wikitable"
|+
!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===
{| 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
|}

===  Requirements===

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

== 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
|WorkGroup=IA et cybersécurité
}}