10 Years of IoT Malware: A Feature-Based Taxonomy
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
Over the past decade, there has been a rapidly growing interest in IoT-connected devices. But as is usually the case with computer systems and networks, malicious individuals soon noticed that these objects could be exploited for criminal purposes. The problem is particularly salient since the firmware used in many Internet connected devices were developed without taking into consideration the expertise and best security practices gained over the past several years by programmers in other areas. Multiple attacks on IoT devices took place therefore over the last decade, culminating with the largest ever recorded DDoS attack, the Mirai botnet, which took advantage of the weaknesses in the security of the IoT. In this survey, we seek to shed light on the evolution of the IoT malware. We compare the characteristic features of 16 of the most widespread IoT malware programs of the last decade and propose a novel methodology for classifying malware based on its behavioral features. Our study also highlights the common practice of feature reuse across multiple malware programs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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