Gustavo Bertoli, Lourenço Pereira, Filipe Verri, Aldri Santos, Osamu Saotome
We lifted 1 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| c2dc/ab-trap | canonical | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| create_dataframe | Not yet run | c2dc/ab-trap/1_Attack dataset/Internet/generate_dataset.py pointer only (licence: NONE) · get_code("d37b35a6e4d2e6ee") |
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Most research using machine learning (ML) for network intrusion detection systems (NIDS) uses well-established datasets such as KDD-CUP99, NSL-KDD, UNSW-NB15, and CICIDS-2017. In this context, the possibilities of machine learning techniques are explored, aiming for metrics improvements compared to the published baselines (model-centric approach). However, those datasets present some limitations as aging that make it unfeasible to transpose those ML-based solutions to real-world applications. This paper presents a systematic data-centric approach to address the current limitations of NIDS research, specifically the datasets. This approach generates NIDS datasets composed of the most recent network traffic and attacks, with the labeling process integrated by design.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2110.13655")
get_code_for_paper("2110.13655")
have("2110.13655")
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