Evaluating Moving Target Defense Methods Using Time to Compromise and Security Risk Metrics in IoT Networks
Bibliographic record
Abstract
The Internet of Things (IoT) networks face an increasing number of cyber threats due to their heterogeneous, distributed, and resource-constrained nature. Conventional static defense mechanisms are often inadequate against sophisticated and advanced persistent threats. Moving Target Defense (MTD) is a dynamic proactive security method that increases system resilience by continuously changing the attack surface, thereby increasing uncertainty and complexity for attackers. In this paper, we evaluate the effectiveness of shuffling or diversity-based MTD methods using time-to-compromise and security risk metrics. We develop attack path-based mean time-to-compromise and security risk reduction metrics for assessing the effectiveness of MTD. These metrics provide a quantitative basis for evaluating how well MTD techniques delay successful compromises and lower overall security risk exposure. The performance of the deployed MTD mechanism is evaluated and discussed for different attacker skill levels and shuffling frequencies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".