Sybil attacks as a mitigation strategy against the Storm botnet
Bibliographic record
Abstract
The Storm botnet is one of the most sophisticated botnet active today, used for a variety of illicit activities. A key requirement for these activities is the ability by the botnet operators to transmit commands to the bots, or at least to the various segmented portions of the botnet. Disrupting these command and control (C&C) channels therefore becomes an attractive avenue to reducing botnets effectiveness and efficiency. Since the command and control infrastructure of Storm is based on peer-to-peer (P2P) networks, previous work has explored the use of index poisoning, a disruption method developed for file-sharing P2P networks, where the network is inundated with false information about the location of files. In contrast, in this paper we explore the feasibility of Sybil attacks as a mitigation strategy against Storm. The aim here is to infiltrate the botnet with large number of fake nodes (sybils), that seek to disrupt the communication between the bots by inserting themselves in the peer lists of ldquoregularrdquo bots, and eventually re-reroute or disrupt ldquorealrdquo C&C traffic. An important difference with index poisoning attacks is that sybil nodes must remain active and participate in the underlying P2P protocols, in order to remain in the peer list of regular bot nodes. However, they do not have to respond to the botmasterpsilas commands and participate into illicit activities. First, we outline a methodology for mounting practical Sybil attacks on the Storm botnet. Then, we describe our simulation studies, which provide some insights regarding the number of sybils necessary to achieve the desired level of disruption, with respect to the net growth rate of the botnet. We also explore how certain parameters such as the duration of the Sybil attack, and botnet design choices such as the size of a botpsilas peer list, affect the effectiveness of the attack.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".