Rule Mode Selection in Intrusion Detection and Prevention Systems
Bibliographic record
Abstract
Protection and performance are the major requirements for any Intrusion Detection and/or Prevention System (IDPS). Existing IDPSs do not seem to provide a satisfactory method of achieving these two conflicting goals. Intrusion Detection Systems (IDSs) fulfill the network performance requirement but exhibit poor protection under successive attacks. On the other hand, Intrusion Prevention Systems (IPSs) can protect the network by dropping the malicious packets that match any attacking pattern; however, this can have a negative impact on network performance in terms of delay as the attacking patterns increase. This results in a tradeoff between security enforcement levels on one hand and the performance and usability of an enterprise information system on the other. This paper aims to study the impact of security enforcement levels on the performance and usability of an enterprise information system. We propose a rule mode selection optimization technique that aims to determine an appropriate IDPS configuration set in order to maximize the security enforcement levels while avoiding any unnecessary network performance degradation. Simulation was conducted to validate our proposed technique. The results demonstrate that it is desirable to strike a balance between system security and network performance.
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 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".