Evolutionary optimization of on-line multilayer perceptron for similarity-based access control

Conventional Multilayer Perceptron training is not efficient when dealing with data streams such that access patterns flow since the availability of the data samples is limited. Considering this obstacle I proposed to use Genetic Algorithm as meta-heuristic optimization in selection of individual training rates 'alpha' for each weight. Similarity-based Access Control mechanism deals with a data stream that includes continuous flow of attributes characterizing user and resources, so the task is to estimate the likelihood of legitimacy of user accessing a particular resource in dynamic environment. This research contributes to the field of Information Security by overcoming the limitations of data stream mining in agile environment.

You can find more information about the practical experiments and datasets in the following conference paper:

@inproceedings{shalaginov2017evolutionary,
title={Evolutionary optimization of on-line multilayer perceptron for similarity-based access control},
author={Shalaginov, Andrii},
booktitle={2017 International Joint Conference on Neural Networks (IJCNN)},
pages={823--830},
year={2017},
organization={IEEE}
}

The orginal dataset that was used for this task is Amazon.com - "Employee Access Challenge": https://www.kaggle.com/c/amazon-employee-access-challenge Corresponding pre-procesed files are train.txt and test.txt

• g++ (tested on v. 4.7.3 and higher)
• STL containers for data operations
• OpenMP for parallel execution (v. 3.1 and higher)