01427nam a2200169 a 4500 20260901030339.0 250101s2019 xx o 000 0 eng d Carlos Garcia Cordero On generating network traffic datasets with synthetic attacks for intrusion detection arXiv 2019 text online resource Most research in the area of intrusion detection requires datasets to develop, evaluate or compare systems in one way or another. In this field, however, finding suitable datasets is a challenge on to itself. Most publicly available datasets have negative qualities that limit their usefulness. In this article, we propose ID2T (Intrusion Detection Dataset Toolkit) to tackle this problem. ID2T facilitates the creation of labeled datasets by injecting synthetic attacks into background traffic. The injected synthetic attacks blend themselves with the background traffic by mimicking the background traffic's properties to eliminate any trace of ID2T's usage. This work has three core contribution areas. First, we present a comprehensive survey on intrusion detection datasets. In the survey, we propose a classification to group the negative qualities we found in the datasets. Second, the archite Open access — freely available to read. https://arxiv.org/pdf/1905.00304v1 Read the full paper (PDF) ERES 658 658 0 0 0 3 CYB MAIN MAIN SCICOMP 2026-09-01 0 YGE000919 2026-09-01 03:03:39 https://arxiv.org/pdf/1905.00304v1 2026-09-01 PAPER