Analysis of NIST Lightweight Cryptographic Algorithms Performance in IoT Security Environments based on MQTT
Bibliographic record
Abstract
In this vision paper, we analyze the protocols used in the Internet of Things (IoT), encryption methods, and their combination for exploitation. The Internet of Things (IoT) is an important paradigm of modern technology that connects physical objects and devices into a single network where they can exchange data and interact without direct human intervention. The Internet of Things is used in a variety of areas, from controlling household appliances to monitoring the condition of objects in industry and agriculture. The MQTT (Message Queuing Telemetry Transport) protocol was used in this work, which is a lightweight protocol for transmitting messages in IoT networks. It allows for efficient data exchange between devices, ensuring low energy consumption and minimizing bandwidth. The following ciphers were used to ensure the security of information in IoT networks: ASCON and Grain128-AEAD. ASCON is used to encrypt and authenticate data, ensuring its confidentiality and integrity. Grain128-AEAD is also used for data protection, providing a high level of security and encryption. This research work simulates an IoT environment based on the MQTT communication protocol and tests the performance of lightweight cryptographic algorithms. As evident from the results, encryption is an essential part of security for IoT -based communication systems and such lightweight algorithms could help in boosting the overall performance with low to none cases of failure. This paper looks to envision the suitability of these lightweight cryptographic (LWC) security and privacy solutions for IoT and Cyber-Physical Systems (CPS).
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".