Collaborative architecture for distributed intrusion detection system
Bibliographic record
Abstract
Due to the rapid growth of network technologies and substantial improvement in attack tools and techniques, a distributed intrusion detection system (dIDS) is required to allocate multiple IDSs across a network to monitor security events and to collect data. However, dIDS architectures suffer from many limitations such as the lack of a central analyzer and a heavy network load. In this paper, we propose a new architecture for dIDS, called a collaborative architecture for dIDS (C-dIDS), to overcome these limitations. The C-dIDS contains one-level hierarchy dIDS with a non-central analyzer. To make the detection decision for a specific IDS module in the system, this IDS module needs to collaborate with the IDS in the lower level of the hierarchy. Cooperating with lower level IDS module improves the system accuracy with less network load (just one bit of information). Moreover, by using one hierarchy level, there is no central management and processing of data so there is no chance for a single point of failure. We have examined the feasibility of our dIDS architecture by conducting several experiments using the DARPA dataset. The experimental results indicate that the proposed architecture can deliver satisfactory system performance with less network load.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".