Overview and Exploratory Analyses of CICIDS2017 Intrusion Detection Dataset
Bibliographic record
Abstract
Intrusion detection systems are used to detect attacks in a network. Machine learning (ML) approaches have been widely used to build such Intrusion Detection Systems (IDSs) because they are more accurate when built from a very large and representative dataset. In recent times, one of the benchmark datasets that are used to build ML-based Intrusion Detection models is CICIDS2017 dataset. The dataset is contained in eight groups and was collected from the Canadian Institute on Cyber Security dataset repository. The dataset is available in both PCAP and netflow formats. This study used the netflow records in the CIDIDS2017 dataset as they are found to contain newer attacks,very large and are found useful for traffic analysis. Exploratory Data Analysis (EDA) techniques were used to reveal various characteristics of the dataset. The general objective is to provide more insights on the nature, structure and issues with the dataset so as to identify the best ways for using it to achieve improved ML-based IDS models. Furthermore, some of the open problems that can arise from the use of the dataset in any machine learning-based Intrusion Detection systems are highlighted and possible solutions are briefly discussed. The EDA techniques used revealed important relationships among input variables and the target class. The study concluded that the EDA can better influence the decision of future IDS researches that use the dataset. Thus, improved machine learning-based intrusion detection systems can be built from the dataset once it is well understood and pre-processed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".