Botnets in 4G cellular networks: Platforms to launch DDoS attacks against the air interface
Bibliographic record
Abstract
Botnets are overlay networks built by cyber-criminals from hacked smartphones and computers. In this paper, we report a vulnerability of the air interface of 4G cellular networks, the Long Term Evolution (LTE), to Distributed Denial-of-Service (DDoS) attacks launched from botnets. The attack scenario constitutes of a bot-master instructing the botnet nodes to start sending or downloading dummy data in order to overwhelm the air interface, thereby denying service for voice users. Through simulation using a capable LTE simulator, we determine the number of botnet nodes needed per cell that can effectively render the cellular network unusable. Specifically, we show that a botnet that has spread to only 3% of subscribers is capable of lowering the voice quality from 4.3 to 2.8 in Mean Opinion Score (MOS) scale of 1 to 5 for scheduling strategies designed for realtime flows. On the other hand, a botnet that has managed to spread to 6% of subscribers can cause a MOS value of 1, i.e., a complete outage. The threat identified and the reported results could inspire the implementation of new mechanisms to ensure the security and availability of vital telecommunication services.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".