"\"CIC-DDoS2019 Non-Scaled Balanced 8 Attack Subset\""
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
"The CIC-DDoS2019 dataset was initially released by the Canadian Institute for Cybersecurity. On the testing day, seven DDoS attacks were executed, while twelve occurred on the training day to compile this dataset. The uploaded data titled \"CIC-DDoS2019 Non-Scaled Balanced 8 Attack Subset\" is prepared by applying a series of modification steps on the training day traffic of CIC-DDoS2019 dataset. These steps encompass:Data integration- Data is integrated from 12 CSV files which are generated by the network traffic of training day.Preprocessing:Removal of spaces in column names.Removal of 10 non-informative columns: 'Unnamed:0', \"FlowID\", \"SourceIP\", \"DestinationIP\", 'FwdAvgBytes\/Bulk', 'FINFlagCount', 'FwdAvgBulkRate', 'PSHFlagCount', 'Timestamp', 'SimillarHTTP'.Removal of rows with infinite valuesRemoval of duplicate rowsSelection of a subset of data comprising benign traffic along with eight specific attacks: NTP, DNS, LDAP, MSSQL, NetBIOS, SNMP, SSDP, UDP, UDP-Lag, WebDDoS, SYN, and TFTP.Drop the highly correlated 30 features from the Dataset. Number of features after dropping highly correlated ones are 47.Dataset with 47 features are down sampled with random under sampling to produce the final balanced dataset."
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it