A lightweight encryption algorithm for resource-constrained IoT devices using quantum and chaotic techniques with metaheuristic optimization
Bibliographic record
Abstract
As the internet of things (IoT) continues to proliferate, the need for efficient and secure data encryption has become increasingly critical, particularly for resource-constrained devices. Existing encryption methods offer adequate security for digital data; however, they often fall short when applied to resource-constrained IoT devices. This research introduces a novel lightweight encryption algorithm optimized with metaheuristic techniques, incorporating quantum encryption, confusion and diffusion operations, discrete wavelet transform (DWT), and multiple chaotic maps. Initially, a color image is decomposed into its three color components-red (R), green (G), and blue (B)-and then transformed into its quantum representation, where quantum encryption operations are performed. Following this, the quantum image is transformed back into a classical format to apply confusion and diffusion techniques. Confusion is achieved by generating a substitution matrix and applying a modular operation to introduce pixel-level confusion. A key matrix is then created to implement the diffusion operation. In the final phase, DWT is used to extract frequency sub-bands, forming a low-frequency sub-band and further extracting sub-bands up to the 4th level, which are substituted using values from the substitution box. The performance of the proposed encryption framework is evaluated through various statistical analyses, including entropy, correlation, key sensitivity, lossless analysis, and histogram analysis. The results demonstrate notable statistical measures with an entropy of 7.9998, a correlation of 0.0001, and a key space of [Formula: see text]. Additionally, the encryption's robustness is tested against several cyberattacks, such as noise, cropping, and brute force, showcasing its effectiveness in resisting these threats.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".