Diet-ESP: IP layer security for IoT
Bibliographic record
Abstract
The number of devices connected through the Internet of Things (IoT) will significantly grow in the next few years while security of their interconnections is going to be a major challenge. For many devices in IoT scenarios, the necessary resources to send and receive bytes are extremely high and when such devices are powered with battery the amount of exchanged bytes directly impacts their life time. As a result, compression of existing protocols is a widely accepted technique to make IoT benefit from the protocols developed over the last decades. This paper presents ESP Header Compression (EHC), a framework that enables compression of packets protected with Encapsulating Security Payload (ESP). EHC is composed of EHC Rules, targeting the compression of a specific field and organized according to EHC Strategies. Further, the paper presents Diet-ESP, an EHC Strategy that highly reduces the networking overhead of ESP packets to address the IoT security and bandwidth requirements. Diet-ESP results in sending fewer bytes which in turn reduces the number of required radio frames and thus battery consumption. The measurements showed that sending 10 byte application data on IEEE 802.15.4 radio networks secured with the standard ESP requires sending an additional frame. This results into a 95% energy overhead compared to the unprotected data, while Diet-ESP results only in a 3% overhead compared to unprotected data. This small overhead is achievable with some compressions being performed within the ESP stack which requires altering the same. Nevertheless, Diet-ESP remains fully security compliant to ESP and performs better than any other compression framework as far as ESP is considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".