Lightweight IDS Based on Features Selection and IDS Classification Scheme
Why this work is in the frame
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Bibliographic record
Abstract
The intrusion detection system (IDS) deals with huge amount of data which contains irrelevant and redundant features causing slow training and testing process, higher resource consumption as well as poor detection rate. To overcome these limitations, we introduce the concept of lightweight IDS. The lightweight IDSs are small, powerful, and flexible enough to be used as permanent elements of the network security infrastructure. In this paper, we propose a novel concept for building lightweight IDS based on two different approaches. The first approach is using a features selection approach by applying fuzzy enhanced support vector decision function (Fuzzy ESVDF) algorithm. This approach is able to improve system efficiency. The second approach is using IDS classification scheme. The IDS classification scheme divides the detection process into four types according to the TCP/IP network model (application layer IDS, transport layer IDS, network layer IDS, and link layer IDS). This IDS classification scheme enhances an organizationpsilas ability to detect most types of attack (improving system accuracy and generality). Also, it improves IDS scalability and extendibility. To design the proposed system, several experiments have been conducted, and they indicate that the proposed lightweight IDS can deliver satisfactory system performance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it