Modified Lightweight Advanced Encryption Standard for Lightweight Embedded Applications
Bibliographic record
Abstract
Cryptography enables data integrity, authentication, non-repudiation, and confidentiality.AES, the Advanced Encryption Standard, is part of data confidentiality in cryptography and among the strongest and most effective for implementation difficulty and security for block cipher -the symmetric algorithm that encrypts and decrypts plaintext using the same key.Lightweight AES is a modified AES for lightweight applications like Embedded Systems or the Internet of Things (IoT).Research suggested modifications to 128-bit AES for lightweight applications prioritizing MixColumns, SubBytes, ShiftRows, round reduction, and Key expansion.This paper presents MLAES, the modified lightweight 128-bit AES, by modifying subsets of AES components: the S-box table within the SubBytes function and the MixColumns constants within the MixColumns function, including reductions to number of rounds.Although using a different method, the findings conclude that the MLAES algorithm, which is evaluated on the same dataset consisting of plaintexts and keys as the state-of-the-art, has the result of an average avalanche effect at 53.6719%.MLAES is 0.6250% better than state-of-the-art (53.0469%).
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 | 0.003 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".