A Security Approach for CoAP-based Internet of Things Resource Discovery
Bibliographic record
Abstract
The growth of the Internet of Thing (IoT) results in an expanded attack that requires end-to-end security techniques. IoT applications involve in a business-oriented such as insurance and banking, and mission-critical crisis such as e-health and intelligent transportation systems. One of the most protocols commonly used for resource discovery in IoT is the Constrained Application Protocol (CoAP) protocol which fits the constrained devices. There is a need for security support in CoAP for the IoT environment. This paper presents a security approach using TACACS+ to strengthen the security of CoAP. The proposed security mechanism separately supports access control, authentication, and accounting. It has been implemented using a mobile phone and a Raspberry Pi. The mobile phone is used as a client, and the Raspberry Pi is used as a server. The implementation composes of a TI SensorTag and a WeMo switch that are used as resources. This paper, also, presents performance indexes of the security technique in terms of CPU usage, time computation, latency, energy consumption, and traffic exchange between a client and a server. The experimental results show the proposed method is compatible with IoT devices.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".