Using Visual Analytics to Develop Situation Awareness in Network Intrusion Detection System
Bibliographic record
Abstract
Network Intrusion Detection System (NIDS) is a security system that monitors the network traffic and analyzes activities for possible hostile attacks. A novel collaborative visual analytics application for cognitive overloaded site security officer (SSO) in the network intrusion detection environment is presented. The system was developed for site security officers who need to analyze heterogeneous, complex intrusion under time pressure, and then make predictions and time-critical decisions rapidly and correctly under a constant influx of intrusion alert/alarm. This purpose was achieved by designing system architecture of a Treemaps Visualization on NIDs. The Treemaps Network Intrusion Detection System was implemented using the Java platform. The results of an informal usability of the network system were evaluated by the security experts in the context of Endley’s three levels of situation awareness. The proposed visualization tool has some economic advantages by aiding NID’s SSO to dynamically discover intrusive zone which will reduce cost of manual analysis and high risks, efficient space utilization, interactivity, comprehension and esthetics.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| 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".