Clicktok Eval

---
name: clicktok-eval
description: Evaluates the ability to detect fraudulent ad clicks (clickspam) by analyzing temporal reuse patterns in organic clickstreams. It tests both passive traffic analysis and active bait-click injection strategies to distinguish legitimate user behavior from automated or malware-driven fraud. Use when the user wants to benchmark on University Network Click Traffic Dataset, or asks about evaluating this task. Reports FPR.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 1903.00733
  bibtex_key: [REDACTED]
  confidence: high
---

# clicktok-eval

> Clicktok: Click Fraud Detection using Traffic Analysis — Nagaraja et al. (2019) (arXiv:1903.00733, 2019)

## What this evaluates

Evaluates the ability to detect fraudulent ad clicks (clickspam) by analyzing temporal reuse patterns in organic clickstreams. It tests both passive traffic analysis and active bait-click injection strategies to distinguish legitimate user behavior from automated or malware-driven fraud.

## Datasets

- **University Network Click Traffic Dataset** — total 217334190; splits: evaluation (217334190)

## Metrics

- `FPR` **(primary)** — range: percent
  - False Positive Rate: the fraction of legitimate clicks incorrectly reported as fraudulent. Calculated as FP / (FP + TN).
- `TPR` — range: percent
  - True Positive Rate: the fraction of fraudulent clicks correctly detected. Calculated as TP / (TP + FN).

## Input / output format

**Input**: Time-series click traffic aggregated into a matrix where each row represents a source IP address per day, and each column represents a 5-minute interval (288 columns/day). Each cell contains the total click count. Additional features per click include ad URL, ad server IP, referrer URL, source IP, User-Agent string, and timestamp.

**Output**: Binary classification per click or per time-window: predicted as legitimate (0) or clickspam/fraudulent (1). For active bait-click defense, outputs the fraction of fraud clicks per time-window based on pattern activation weights.

## Scoring recipe

```python
def compute_tpr_fpr(predictions, gold_labels):
    tp = sum(1 for p, g in zip(predictions, gold_labels) if p == 1 and g == 1)
    fp = sum(1 for p, g in zip(predictions, gold_labels) if p == 1 and g == 0)
    fn = sum(1 for p, g in zip(predictions, gold_labels) if p == 0 and g == 1)
    tn = sum(1 for p, g in zip(predictions, gold_labels) if p == 0 and g == 0)
    tpr = tp / (tp + fn) if (tp + fn) > 0 else 0.0
    fpr = fp / (fp + tn) if (fp + tn) > 0 else 0.0
    return {'TPR': tpr, 'FPR': fpr}
```

## Common pitfalls

- Confusing the passive (mimicry) and active (bait-click) evaluation setups, which require different matrix initialization and pattern isolation methods.
- Misinterpreting the temporal granularity: the evaluation bins clicks into 5-minute intervals per source IP per day, not per individual click event.
- Overlooking that the dataset is synthetically generated by exposing legitimate university traffic to known click malware, meaning real-world distribution shift may affect generalization.

## Evidence (verbatim from paper)

> The FP and TP rates are the fraction of legitimate clicks reported as fraudulent clicks, and the fraction of fraudulent clicks detected, respectively.

## Citation

```bibtex
@misc{nagaraja2019clicktok,
  title={Clicktok: Click Fraud Detection using Traffic Analysis},
  author={Nagaraja et al. (2019)},
  year={2019},
  note={arXiv:1903.00733}
}
```

- arXiv: 1903.00733