Analysis of NSL-KDD Dataset Using K-Means and Canopy Clustering Algorithms Based on Distance Metrics

Vinutha, H. P.; Poornima, B.

doi:10.1007/978-981-10-8797-4_21

Cited by 6 publications

(1 citation statement)

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“…Although this is not a recent dataset, it serves as a baseline since it is widely used in the literature to evaluate recent proposals. [38][39][40] From this dataset, three detectors of heterogeneous data sources were configured. The data sources correspond to individual TCP connection features, traffic statistics, and application logs and were used to generate Detectors 1, 2, and 3, respectively.…”

Section: Scenario 1: Heterogeneous Cooperative Networkmentioning

confidence: 99%

Counselors network for intrusion detection

et al. 2020

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Intrusion detection systems (IDSs) are a fundamental component of defense solutions. In particular, IDSs aim to detect malicious activities on computer systems and networks by relying on data classification models built from a training dataset. However, classifiers' performance can vary for each attack pattern. A common technique to overcome this issue is to use ensemble methods, where multiple classifiers are employed and a final decision is taken combining their outputs. Despite the potential advantages of such an approach, its usefulness is limited in scenarios where (i) multiple expert classifiers present divergent results, (ii) all classifiers present poor results due to lack of representative features, or (iii) detectors have insufficient labeled signatures to train their classifiers for a specific attack pattern. In this work, we introduce the concept of a counselors network to deal with conflicts from different classifiers by exploiting the collaboration among IDSs that analyze multiple and heterogeneous data sources. Empirical results demonstrate the feasibility of the proposed architecture in improving the accuracy of the intrusion detection process.

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Section: Scenario 1: Heterogeneous Cooperative Networkmentioning

confidence: 99%