Integrating 6LoWPAN Security with RPL Using The Chained Secure Mode\n Framework
Bibliographic record
Abstract
The IPv6 over Low-powered Wireless Personal Area Network (6LoWPAN) protocol\nwas introduced to allow the transmission of Internet Protocol version 6 (IPv6)\npackets using the smaller-size frames of the IEEE 802.15.4 standard, which is\nused in many Internet of Things (IoT) networks. The primary duty of the 6LoWPAN\nprotocol is packet fragmentation and reassembly. However, the protocol standard\ncurrently does not include any security measures, not even authenticating the\nfragments immediate sender. This lack of immediate-sender authentication opens\nthe door for adversaries to launch several attacks on the fragmentation\nprocess, such as the buffer-reservation attacks that lead to a Denial of\nService (DoS) attack and resource exhaustion of the victim nodes. This paper\nproposes a security integration between 6LoWPAN and the Routing Protocol for\nLow Power and Lossy Networks (RPL) through the Chained Secure Mode (CSM)\nframework as a possible solution. Since the CSM framework provides a mean of\nimmediate-sender trust, through the use of Network Coding (NC), and an\nintegration interface for the other protocols (or mechanisms) to use this trust\nto build security decisions, 6LoWPAN can use this integration to build a\nchain-of-trust along the fragments routing path. A proof-of-concept\nimplementation was done in Contiki Operating System (OS), and its security and\nperformance were evaluated against an external adversary launching a\nbuffer-reservation attack. The results from the evaluation showed significant\nmitigation of the attack with almost no increase in power consumption, which\npresents the great potential for such integration to secure the forwarding\nprocess at the 6LoWPAN Adaptation Layer\n
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".