The challenges of using an intrusion detection system
Bibliographic record
Abstract
An intrusion detection system (IDS) can be a key component of security incident response within organizations. Traditionally, intrusion detection research has focused on improving the accuracy of IDSs, but recent work has recognized the need to support the security practitioners who receive the IDS alarms and investigate suspected incidents. To examine the challenges associated with deploying and maintaining an IDS, we analyzed 9 interviews with IT security practitioners who have worked with IDSs and performed participatory observations in an organization deploying a network IDS. We had three main research questions: (1) What do security practitioners expect from an IDS?; (2) What difficulties do they encounter when installing and configuring an IDS?; and (3) How can the usability of an IDS be improved? Our analysis reveals both positive and negative perceptions that security practitioners have for IDSs, as well as several issues encountered during the initial stages of IDS deployment. In particular, practitioners found it difficult to decide where to place the IDS and how to best configure it for use within a distributed environment with multiple stakeholders. We provide recommendations for tool support to help mitigate these challenges and reduce the effort of introducing an IDS within an organization.
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 | 0.044 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".