Real-time signature-based detection approach for SMS botnet
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
As an open platform for mobile electronic devices, Android is experiencing a steady growth in the number of published applications (apps). Features of the Android platform have caught the attention of malicious users who have targeted the Short Message Service (SMS) to abuse its permissions. Various types of attack, referred to as botnets, can be executed without the user's knowledge by taking advantage of SMS messages, such as sending text message spam, transferring all command and control (C&C) instructions, launching denial-of-service (DoS) attacks, sending premium-rate SMS messages, or distributing malicious applications via URLs embedded in text messages. In this paper, we propose a real-time signature-based detection mechanism to combat SMS botnets, in which we first apply pattern-matching detection approaches for incoming and outgoing SMS text messages, and then use rule-based techniques to label unknown SMS messages as suspicious or normal. This approach was evaluated using over 12,000 test messages. It was able to detect all 747 malicious SMS messages in the dataset (100% detection rate with no false negatives). It also flagged 351 SMS messages as suspicious.
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 | 0.000 | 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