Intrusion Detection Systems: A Cross-Domain Overview
Bibliographic record
Abstract
Nowadays, network technologies are essential for transferring and storing various information of users, companies, and industries. However, the growth of the information transfer rate expands the attack surface, offering a rich environment to intruders. Intrusion detection systems (IDSs) are widespread systems able to passively or actively control intrusive activities in a defined host and network perimeter. Recently, different IDSs have been proposed by integrating various detection techniques, generic or adapted to a specific domain and to the nature of attacks operating on. The cybersecurity landscape deals with tremendous diverse event streams that exponentially increase the attack vectors. Event stream processing (ESP) methods appear to be solutions that leverage event streams to provide actionable insights and faster detection. In this paper, we briefly describe domains (as well as their vulnerabilities) on which recent papers were-based. We also survey standards for vulnerability assessment and attack classification. Afterwards, we carry out a classification of IDSs, evaluation metrics, and datasets. Next, we provide the technical details and an evaluation of the most recent work on IDS techniques and ESP approaches covering different dimensions (axes): domains, architectures, and local communication technologies. Finally, we discuss challenges and strategies to improve IDS in terms of accuracy, performance, and robustness.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".