# Resource Aware Ids Allocation Eval

> Evaluates an integer linear programming model for allocating network monitoring depth across protocol layers, balancing detection efficiency against computational resource constraints on a synthetic heterogeneous network. Use when the user wants to benchmark on Synthetic 6-Device Network, or asks about evaluating this task. Reports objective function value.

- Skill: `qhjqhj00/resource-aware-ids-allocation-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/resource-aware-ids-allocation-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/resource-aware-ids-allocation-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/resource-aware-ids-allocation-eval

---


# resource-aware-ids-allocation-eval

> Resource-Aware Layered Intrusion Detection Allocation Model — Pădurean et al. (2026) (arXiv:2604.22304, 2026)

## What this evaluates

Evaluates an integer linear programming model for allocating network monitoring depth across protocol layers, balancing detection efficiency against computational resource constraints on a synthetic heterogeneous network.

## Datasets

- **Synthetic 6-Device Network** — total 6; splits: test (6)

## Metrics

- `objective function value` **(primary)** — range: other
  - Maximizes weighted detection probability minus monitoring costs, subject to budget and feasibility constraints. Exact formula not provided in text.

## Input / output format

**Input**: Device importance weights, attack probabilities, layer-specific detection rates, per-layer monitoring costs, total resource budget, critical device minimum depth, and per-device feasibility caps.

**Output**: Binary decision variables indicating the selected monitoring layer for each device, and the resulting objective function value.

## Scoring recipe

```python
def compute_metric(allocation, budget, costs):
    total_cost = sum(costs[layer] for layer in allocation.values() if layer > 0)
    if total_cost > budget:
        return None  # Infeasible per paper
    # Return the objective function value reported by the solver
    return solver_objective_value
```

## Common pitfalls

- The evaluation relies on a fixed synthetic network rather than a standard benchmark, limiting direct comparison with other models.
- The exact mathematical formulation of the objective function is omitted, making exact metric reproduction difficult.
- A budget of R=5 yields no feasible solution, which is a constraint violation rather than a performance metric.

## Evidence (verbatim from paper)

> The model is instantiated on a small heterogeneous network in order to illustrate its behavior. The four monitoring layers l∈{1,2,3,4} are associated with detection rates d1=0.2, d2=0.5, d3=0.8, d4=0.95 and with monitoring costs c1=1, c2=2, c3=4, c4=7, reflecting the assumption that deeper inspection yields higher detection but at a higher resource cost. The total monitoring budget is set to R∈{5,10,15,20,25,30,35,40}. Critical devices must be monitored at least up to layer α=2... In Fig. 2, the contribution of each device to the objective function value based on the resource budget is illustrated... If resource budget is too small, the model yields no solution (R=5).

## Citation

```bibtex
@misc{padurean2026resource,
  title={Resource-Aware Layered Intrusion Detection Allocation Model},
  author={Pădurean et al. (2026)},
  year={2026},
  note={arXiv:2604.22304}
}
```

- arXiv: 2604.22304