S-MQTT: A Secure MQTT Protocol with Merkle Tree Authentication and AES Encryption for IoT Communication Systems
Bibliographic record
Abstract
As the number of Internet of Things (IoT) devices grows, there is a greater need for secure and efficient communication protocols.A growing number of people are using the Message Queuing Telemetry Transport (MQTT) protocol because of its real-time and lightweight data sharing capabilities.However, security concerns, particularly in scenarios involving the transmission of sensitive information, necessitate the development of augmented security measures.This research introduces a pioneering protocol, Secured MQTT (S-MQTT), designed to address vulnerabilities inherent in the traditional MQTT protocol.To protect the confidentiality, integrity, and authenticity of transmitted data, S-MQTT combines sophisticated encryption methods with access control and authentication protocols.The proposed system S-MQTT in this research employs the MQTT protocol for data transfer within a communication system, comprising three key components: Publisher, Broker, and Subscriber.The study focuses on optimizing time-consuming procedures within the system and fortifying data security in communication systems.Using a Watchdog timer and AES data security, the investigation seeks to assess the broker's dependability in terms of activity level.Comparative analysis of the proposed system against the current system demonstrates superior performance.The results shows that the proposed protocol achieved an overall mitigation efficiency of 97.78%, completely blocking man-in-themiddle attacks and reducing malware intrusions by 96.61%.Encryption and authentication added only minimal latency and moderate resource overhead while significantly enhancing confidentiality, integrity, and availability.Including these metrics in the abstract will provide a balanced view of both the security effectiveness and performance trade-offs of S-MQTT.Additionally, the study presents an assessment of the time and space complexity of the suggested system design.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".