UC7 : Suspicious security events detection

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

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:

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

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

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

Groupe de travail

IA et cybersécurité