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 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".