A DoS Attack Detection Method Based on Multi-source Data Fusion

Chen, Kaiyuan; Gu, Lixu; Sun, Jiaxuan

doi:10.1145/3424978.3425098

Cited by 2 publications

(1 citation statement)

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“…The authors in [ 26 ] exploit decision level data fusion scheme for the denial of service (DoS) attacks. The decisions of basic probability (BP) neural network and D-S evidence theory are combined for the improved results.…”

Section: Related Workmentioning

confidence: 99%

MIND: A Multi-Source Data Fusion Scheme for Intrusion Detection in Networks

Anjum

Latif

Lee

et al. 2021

Sensors

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In recent years, there is an exponential explosion of data generation, collection, and processing in computer networks. With this expansion of data, network attacks have also become a congenital problem in complex networks. The resource utilization, complexity, and false alarm rates are major challenges in current Network Intrusion Detection Systems (NIDS). The data fusion technique is an emerging technology that merges data from multiple sources to form more certain, precise, informative, and accurate data. Moreover, most of the earlier intrusion detection models suffer from overfitting problems and lack optimal detection of intrusions. In this paper, we propose a multi-source data fusion scheme for intrusion detection in networks (MIND) , where data fusion is performed by the horizontal emergence of two datasets. For this purpose, the Hadoop MapReduce tool such as, Hive is used. In addition, a machine learning ensemble classifier is used for the fused dataset with fewer parameters. Finally, the proposed model is evaluated with a 10-fold-cross validation technique. The experiments show that the average accuracy, detection rate, false positive rate, true positive rate, and F-measure are 99.80%, 99.80%, 0.29%, 99.85%, and 99.82% respectively. Moreover, the results indicate that the proposed model is significantly effective in intrusion detection compared to other state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%