The case for in-the-lab botnet experimentation
Bibliographic record
Abstract
Botnets constitute a serious security problem. A lot of effort has been invested towards understanding them better, while developing and learning how to deploy effective counter-measures against them. Their study via various analysis, modelling and experimental methods are integral parts of the development cycle of any such botnet mitigation schemes. It also constitutes a vital part of the process of understanding present threats and predicting future ones. Currently, the most popular of these techniques are botnet studies, where researchers interact directly with real-world botnets. This approach is less than ideal, for many reasons that we discuss this paper, including scientific validity, ethical and legal issues. Consequently, we present an alternative approach employing in the lab experiments involving at-scale emulated botnets. We discuss the advantages of such an approach over reverse engineering, analytical modelling, simulation and in-the-wild studies. Moreover, we discuss the requirements that facilities supporting them must have. We then describe an experiment which we emulated a 3000-node, fully-featured version of the Waledac botnet, complete with an emulated command and control (C&C) infrastructure. By observing the load characteristics and yield (rate of spamming) of such a botnet, we can draw interesting conclusions about its real-world operations and design decisions made by its creators. Furthermore, we conducted experiments with sybil attacks launched against it and verified their viability. However, we were able to determine that mounting such attacks is not so simple: high resource consumption can cause havoc and partially neutralise them. Finally, we were able to repeat the attacks with varying parameters, an attempt to optimise them. The merits of this experimental approach is underlined since by the fact that it would have been difficult to obtain these results by other methods.
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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 | 0.000 | 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 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".