Socellbot: A new botnet design to infect smartphones via online social networking
Bibliographic record
Abstract
Given the popularity of both smartphones and online social networking, it is only a matter of time before attackers exploit both to launch new types of attacks. In this paper, we propose a new cellular botnet named SoCellBot that exploits online social networks (OSNs) to recruit bots and uses OSN messaging systems as communication channels between bots. Our proposed botnet is the first that uses the OSN platform as a means to control cellular bots. The structure and characteristics of OSNs make this botnet harder to detect, more resilient to bot failures and more cost-effective to cellular bots. Our objective is to raise awareness of new mobile botnets that exploit OSNs to recruit bots so that preventive measures can be implemented to deter this kind of attack in the future. We also analyze the behaviors of the proposed botnet via simulation to offer a better understanding of this new type of botnet.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".