A Formal Analysis of the Efficacy of Rebooting as a Countermeasure Against IoT Botnets
Bibliographic record
Abstract
The Mirai botnet revolutionized the idea of IoT botnets by infecting numerous vulnerable IoT devices in 2016, leading to the rise of many Mirai variants and imitators that plague the current IoT ecosystem. Studying the botnet infection process can greatly aid us in understanding IoT botnet capabilities and the efficacy of currently available countermeasures. However, analyzing IoT botnets is difficult due to their massive scale and the numerous existing heterogeneous IoT devices that can be targeted for infection. In this paper, we model and simulate the dynamic behavior of a Mirai-like botnet infrastructure and various IoT device categories as a network of timed automata in UPPAAL-SMC. To determine the feasibility of rebooting as a countermeasure against botnets, we examine the effectiveness of rebooting on various IoT device networks. The resulting analysis provides a solid understanding of the efficacy and feasibility of rebooting on active and dormant botnet propagation processes.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".