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@article{1833378,
author = {Bangui, Hind and Bühnová, Barbora},
article_location = {England},
article_number = {March 2022},
doi = {http://dx.doi.org/10.1016/j.compeleceng.2022.107901},
keywords = {Mobile edge computing; Deep learning; Bio-inspired computing},
language = {eng},
issn = {0045-7906},
journal = {Computers and Electrical Engineering},
title = {Lightweight Intrusion Detection for Edge Computing Networks using Deep Forest and Bio-Inspired Algorithms},
url = {https://doi.org/10.1016/j.compeleceng.2022.107901},
volume = {100},
year = {2022}
}
TY - JOUR
ID - 1833378
AU - Bangui, Hind - Bühnová, Barbora
PY - 2022
TI - Lightweight Intrusion Detection for Edge Computing Networks using Deep Forest and Bio-Inspired Algorithms
JF - Computers and Electrical Engineering
VL - 100
IS - March 2022
SP - 107901
EP - 107901
PB - Elsevier
SN - 00457906
KW - Mobile edge computing
KW - Deep learning
KW - Bio-inspired computing
UR - https://doi.org/10.1016/j.compeleceng.2022.107901
N2 - Today, incorporating advanced machine learning techniques into intrusion detection systems (IDSs) plays a crucial role in securing mobile edge computing systems. However, the mobility demands of our modern society require more advanced IDSs to make a good trade-off between coping with the rapid growth of traffic data and responding to attacks. Thus, in this paper, we propose a lightweight distributed IDS that exploits the advantages of centralized platforms to train and learn from large amounts of data. We investigate the benefits of two promising bio-inspired optimization algorithms, namely Ant Lion Optimization and Ant Colony Optimization, to find the optimal data representation for the classification process. We use Deep Forest as a classifier to detect intrusive actions more robustly and generate as few false positives as possible. The experiment results show that the proposed approach can enhance the reliability of lightweight intrusion detection systems in terms of accuracy and execution time.
ER -
BANGUI, Hind and Barbora BÜHNOVÁ. Lightweight Intrusion Detection for Edge Computing Networks using Deep Forest and Bio-Inspired Algorithms. \textit{Computers and Electrical Engineering}. England: Elsevier, 2022, vol.~100, March 2022, p.~107901-107917. ISSN~0045-7906. Available from: https://dx.doi.org/10.1016/j.compeleceng.2022.107901.
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