Analysis on Network Traffic Features for Designing Machine Learning based IDS
Bibliographic record
Abstract
Abstract An intrusion detection system (IDS) is the most important technology for securing network systems. It can dynamically monitor network traffic for malicious activities that are aimed to violate confidentiality, integrity, authenticity, and availability of the network. Currently, several Machine Learning (ML) techniques are used to design and implement IDS since ML techniques can capture the complex nature of cyberattacks. However, network traffic information usually contains unimportant features that can deteriorate the efficacy of ML-based IDS. This research analyses the critical features in network traffic to be used for design/implementing the effective ML-based IDS. The selected features are applied to different ML methods to test the effectiveness. This research is conducted on the CICIDS2017 dataset generated by the Canadian Institute of Cybersecurity, using 30 percent of the full datasets and 100 percent of the Wednesday set. The best result achieved for 30 percent of the full set is by using 30 chosen features with the Bagging ensemble classifier giving the accuracy of 99.9 percent with the false-positive rate as low as 0.03 percent. The best result achieved for Wednesday set is by using the Random Forest Classifier which achieves an accuracy of 99.9 percent and a false-positive rate (FPR) of 0.02 percent.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".