SMS mobile botnet detection using a multi-agent system
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
With the enormous growth of Android mobile devices and the huge increase in the number of published applications (apps), Short Message Service (SMS) is becoming an important issue. SMS can be abused by attackers when they send SMS spam, transfer all command and control (C&C) instructions, launch denial-of-service (DoS) attacks to send premium-rate SMS messages without user permission, and propagate malware via URLs sent within SMS messages. Thus, SMS has to be reliable as well as secure. In this paper, we propose a SMS botnet detection framework that uses multi-agent technology based on observations of SMS and Android smartphone features. This system detects SMS botnets and identifies ways to block the attacks in order to prevent damage caused by these attacks. An adaptive hybrid model of SMS botnet detectors is being developed by using a combination of signature-based and anomaly-based methods. The model is designed to recognize malicious SMS messages by applying behavioural analysis to find the correlation between suspicious SMS messages and reported profiling. Behaviour profiles of Android smartphones are being created to carry out robust and efficient anomaly detection. A multi-agent system technology was selected to perform light-weight detection without exhausting smartphone resources such as battery and memory.
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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 | 0.000 | 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 it