Abstract: The booming of computer networks and software applications has led to an explosive growth in the potential damage caused by network attacks. Efficient detection of abnormal traffic in networks is appealing for facilely mastering the traffic tracking and locating for network usage at low resource cost. High quality abnormal traffic detection of Internet becomes particularly relevant during the automated services of multiple application situations. This paper proposes a novel abnormal traffic detection algorithm called ODFM based on the optimization of data feature and mining. Specially, we develop a feature selection strategy to reduce the feature analysis dimension, and set a peer-to-peer (P2P) traffic identification module to filter and mine the related service traffic to reduce the amount of data detection and facilitate the abnormal traffic detection. Experimental results demonstrate that the proposed algorithm greatly improves the detection accuracy, which verifies its effectiveness and competitiveness in the general tasks of abnormal network traffic detection.

Xianzong Wu, “ODFM: Abnormal Traffic Detection Based on Optimization of Data Feature and Mining” International Journal of Advanced Computer Science and Applications(IJACSA), 14(11), 2023. http://dx.doi.org/10.14569/IJACSA.2023.01411112

@article{Wu2023,
title = {ODFM: Abnormal Traffic Detection Based on Optimization of Data Feature and Mining},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.01411112},
url = {http://dx.doi.org/10.14569/IJACSA.2023.01411112},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {11},
author = {Xianzong Wu}
}