Data preprocessing for distance-based unsupervised Intrusion Detection
Bibliographic record
Abstract
Since Intrusion Detection Systems (IDSs) operate in real-time, they should be light-weighted to detect intrusions as fast as possible. Distance-based Outlier Detection (DBOD) is one of the most widely-used techniques for detecting outliers due to its simplicity and efficiency. Additionally, DBOD is an unsupervised approach which overcomes the problem of the lack of training datasets with known intrusions. However, since IDSs usually have high-dimensional datasets, using DBOD becomes subject to the curse of the dimensionality problem. Furthermore, intrusion datasets should be normalized before calculating pair-wise distance between observations. The purpose of this research is conduct a comparative study among different normalization methods in conjunction with a well-known feature extraction technique; Principle Component Analysis (PCA). Therefore, the efficiency of these methods as data preprocessing techniques can be investigated when applying DBOD to detect intrusions. Experiments were performed using two kinds of distance metrics; Euclidean distance and Mahalanobis distance. We further examined the PCA using 7 threshold values to indicate the number of Principle components to consider according to their total contribution in the variability of features. These approaches have been evaluated using the KDD Cup 1999 intrusion detection (KDD-Cup) dataset. The main purpose of this study is to find the best attribute normalization method along with the correct threshold value for PCA so that a fast unsupervised IDS can discover intrusions effectively. The results recommended using the Log normalization method combined the Euclidean distance while performing PCA.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".