Abstract
The dendritic cell algorithm is an immune inspired method based on the danger model, which relies on cell interactions to antigens and signals, considering the correlation between both events, to solve anomaly detection problems. Starting with new datasets, comprising ping scans and file transfers in computer networks, this paper proposes improvements in the algorithm test methodology and qualitative analysis. Results measurement, detection capability, and performance evaluation are explored and discussed, including a real-time analysis and a comparison with similar approaches. Concluding, the study discusses advantages and limitations of the studied approach, suggesting possible improvements and new applications.
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