A Formal Analysis of the Efficacy of Rebooting as a Countermeasure Against IoT Botnets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 it