Publications

@article{velasco-mata_efficient_2021,

title = {Efficient detection of botnet traffic by features selection and decision trees},

author = {Javier Velasco-Mata and Víctor González-Castro and Eduardo Fidalgo and Enrique Alegre},

url = {https://ieeexplore.ieee.org/abstract/document/9523853},

year  = {2021},

date = {2021-01-01},

journal = {IEEE Access},

volume = {9},

pages = {120567–120579},

abstract = {Botnets pose a major online threat, causing significant economic losses. With the rise of connected devices, analyzing large network traffic data is crucial. This study enhances botnet traffic classification by selecting the most relevant features using Information Gain and Gini Importance. Three feature subsets (5, 6, and 7 features) were tested with Decision Tree, Random Forest, and k-Nearest Neighbors on two datasets derived from CTU-13 (QB-CTU13 and EQB-CTU13). Results show that Decision Trees with a five-feature set achieved the best performance, with an 85% F1 score and an average classification time of 0.78 microseconds per sample.},

note = {Publisher: IEEE},

keywords = {Botnet Detection, Cybersecurity, feature selection, machine learning, Network Traffic Analysis},

pubstate = {published},

tppubtype = {article}

}