klm-PPSA Dataset V 1.0

Citation Author(s):
Nahid
Eddermoug
NEST Research Group, LRI Lab., ENSEM, Hassan II University of Casablanca, Morocco
Abdeljebar
Mansour
SIGL Lab., ENSATE, Abdelmalek Essaadi University-Tetouan, Morocco
Mohamed
Sadik
NEST Research Group, LRI Lab., ENSEM, Hassan II University of Casablanca, Morocco
Essaid
Sabir
NEST Research Group, LRI Lab., ENSEM, Hassan II University of Casablanca, Morocco
Mohamed
Azmi
ISSIEE Lab., ENSAM, Hassan II University of Casablanca, Morocco
Submitted by:
Nahid Eddermoug
Last updated:
Fri, 06/10/2022 - 06:31
DOI:
10.21227/1c13-df06
Data Format:
*.csv
License:
Creative Commons Attribution
320 Views
Categories:
Machine Learning
Security
Cloud Computing
Computational Intelligence
Keywords:
klm-PPSA Dataset, intrusion detection systems, cloud computing security, Cyber Security, preventing and profiling attacks, authentication; security; behaviour, Cyber Attacks
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Abstract 

This dataset is used to illustrate an application of the "klm-based profiling and preventing security attack (klm-PPSA)" system. The klm-PPSA system is developed to profile, detect, and then prevent known and/or unknown security attacks before a user access a cloud. This dataset was created based on “a.patrik” user logical attempts scenarios when accessing his cloud resources and/or services. You will find attached the CSV file associated with the resulted dataset. The dataset contains 460 records of 13 attributes (independent and dependent variables). The entries of the dataset contain different features of this user when trying to establish authentications/connections to the system.

For the description of the klm-PPSA system and more details on the dataset, please see the following paper: N. Eddermoug, A. Mansour, M. Sadik, E. Sabir, and M. Azmi, “Klm-based profiling and preventing security attacks for cloud computing: A comparative study,” in 2021 28th International Conference on Telecommunications (ICT), June 2021, pp. 1–6.

Instructions: 

This dataset contains 460 records of 13 attributes (independent and dependent variables), as follows: *Dependent variables: Is the authentication safe (a predetermined qualitative variable equal to Yes or No). *Independent variables: Time, age, Internet Protocol address, place, Media Access Control address, used device, method of authentication, biometric features, keystrokes, k-factor (#attempts with incorrect password), m-factor (#attempts with the correct password but with invalid keystrokes, and l-factor (#attempts using biometric features). The given .csv file can be used to train and/or evaluate the models using machine learning algorithms.

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