Configuration of and Interaction Between Information Security Technologies: The Case of Firewalls and Intrusion Detection Systems
Bibliographic record
Abstract
Proper configuration of security technologies is critical to balance the needs for access and protection of information. The common practice of using a layered security architecture that has multiple technologies amplifies the need for proper configuration because the configuration decision about one security technology has ramifications for the configuration decisions about others. Furthermore, security technologies rely on each other for their operations, thereby affecting each other's contribution. In this paper we study configuration of and interaction between a firewall and intrusion detection systems (IDS). We show that deploying a technology, whether it is the firewall or the IDS, could hurt the firm if the configuration is not optimized for the firm's environment. A more serious consequence of deploying the two technologies with suboptimal configurations is that even if the firm could benefit when each is deployed alone, the firm could be hurt by deploying both. Configuring the IDS and the firewall optimally eliminates the conflict between them, ensuring that if the firm benefits from deploying each of these technologies when deployed alone, it will always benefit from deploying both. When optimally configured, we find that these technologies complement or substitute each other. Furthermore, we find that while the optimal configuration of an IDS does not change whether it is deployed alone or together with a firewall, the optimal configuration of a firewall has a lower detection rate (i.e., allowing more access) when it is deployed with an IDS than when deployed alone. Our results highlight the complex interactions between firewall and IDS technologies when they are used together in a security architecture, and, hence, the need for proper configuration to benefit from these technologies.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".