SMS-Based Mobile Botnet Detection Framework Using Intelligent Agents
Bibliographic record
Abstract
Along with increasing security measures in Android platforms, the amount of Android malware that use remote exploits has grown significantly. Using mobile botnets, attackers concentrate on reliable attack vectors such as SMS messages. Short Message Service (SMS) has been increasingly targeted by a number of malicious applications (“apps”) that have the ability to abuse SMS features in order to send spam, to transfer command and control (C&C) instructions, to distribute malicious applications via URLs embedded in text messages, to send text messages to premium-rate numbers, and to exploit smartphones. In this paper, we propose an SMS-based botnet detection formwork that uses multi-agent technology based on observations of SMS and Android smartphone features. This formwork has the ability to detect SMS botnets and identify ways to block the attacks in order to prevent damage caused by botnet attacks. We developed an adaptive hybrid model of SMS botnet detectors by using a combination of signature-based and anomaly-based algorithms. These components utilize multi-agent technology to recognize malicious SMS and prevent users from opening these messages that infecting smartphones. This framework includes defence module that employed a more proactive approach that allows us to directly generate signatures and rules that can be used to protect Android smartphones from abuse by SMS botnets. The framework creates a user profile that is used to perform behavioural profiling analysis in order to identity malicious SMS and cut the C&C Channel.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".