The effect of buffer management strategies on 6LoWPAN's response to buffer reservation attacks
Bibliographic record
Abstract
The 6L0WPAN adaptation layer is widely used in many Internet of Things (IoT) and vehicular networking applications. The current IoT framework [1], which introduced 6LoWPAN to the TCP/IP model, does not specif the implementation for managing its received-fragments buffer. This paper looks into the effect of current implementations of buffer management strategies at 6LoWPAN's response in case of fragmentation-based, buffer reservation Denial of Service (DoS) attacks. The Packet Drop Rate (PDR) is used to analyze how successful the attacker is for each management technique. Our investigation uses different defence strategies, which include our implementation of the Split Buffer mechanism [2] and a modified version of this mechanism that we devise in this paper as well. In particular, we introduce dynamic calculation for the average time between consecutive fragments and the use of a list of previously dropped packets tags. NS3 is used to simulate all the implementations. Our results show that using a “slotted” buffer would enhance 6LoWPAN's response against these attacks. The simulations also provide an in-depth look at using scoring systems to manage buffer cleanups.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".