Analytical Model for Sybil Attack Phases in Internet of Things
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The sybil attack in Internet of Things (IoT) commonly aims the sensing domain that may impose serious threat to the devices both in perception and communication layer. The singularity of the sybil attack is a sybil node that publish multiple identities of legitimate devices. It is highly essential to learn the behavior and predict possible actions of a sybil attacker while devising a defense mechanism for it. This paper provides a comprehensive characteristic analysis of sybil attack in IoT. Based on the nature of the task performed during this attack, it is classified into three phases as compromise, deployment, and launching phase. The compromise phase is modeled as an automaton with attacker state transition as a Markov chain model. A heuristic is also proposed for selection criteria of an attacker to compromise a node. In the deployment phase of the attack, an algorithm based on K -mean clustering is proposed to group compromised identities and deploy the sybil node for corresponding identities without violating the set of adjacent nodes. In the launching phase, the process of replacing sybil identities either over time or on detection is modeled using age replacement policy. The results depict that the proposed model effectively visualize the behavior of a sybil attacker in challenging environments of IoT.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it