Enhancing Detection Accuracy of Cyber Attacks Through Dimensionality Reduction
Bibliographic record
Abstract
The importance of cyber-security has led to long-standing endeavors dedicated to the design of intrusion detection systems (IDS). Nevertheless, the performance of these data-driven techniques is highly dependent on data quality. Incorporating dimensionality reduction techniques into a hybrid intrusion detection system, we aim to study the effect of dimensionality reduction on the performance of intrusion detection. By this mean, the efficiency of the intrusion detection systems is increased by processing a smaller feature space. Moreover, the reduced feature space also increases the detection accuracy, as redundant and meaningless features are removed in the new feature space. Furthermore, the intrinsic structure of the data is improved, that is different states of the system become more discriminant after dimensionality reduction. For this mean, various state-of-the-art dimensionality reduction techniques are selected. Then, a simulation is performed on a Supervisory Control and Data Acquisition (SCADA) system, which resembles a gas pipeline control system introduced by Morris et al. (2011). A comparative study is then performed to suggest the best dimensionality reduction algorithm in these experiments. The experiments indicate the general improvement of detection accuracy when dimensionality reduction techniques are combined with the IDS in terms of accuracy and standard deviation.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".