Comparative analysis of DES and AES implementations in CyberSecurity applications
Bibliographic record
Abstract
Data encryption is an essential element to a functioning network of networks, otherwise known as the internet. It is important for users to maintain security, whether it be their online assets or secure communication amongst other parties via a network. Data encryption algorithms are put in place to ensure these values, where AES and DES are the two most well known variations. The core purpose of this paper will be to determine why the AES algorithm has proven itself over the DES algorithm as a community preferred standard, mainly in reference to the field of CyberSecurity. This paper will look at the key differences between the DES and AES algorithms. This will include the cost efficiency differences in terms of computational power between the two algorithms, as well as the reason why the AES algorithm is more easily adaptable in today's society. This comparison will be made by dating back to the origins of both algorithms, as well as noting on why AES implementations are typically more accepted in CyberSecurity, as well as other real world sectors such as finance, healthcare and government. Key points will be made on why the DES algorithm has been proven to be susceptible to encryption attacks over time, and why the AES algorithm is able to overcome these faults by looking at their statistical differences in terms of block/key sizes, and the speed at which the algorithms take to encrypt a message. By utilizing real world examples that detail breach attempts on DES algorithms, a clear notion can be established on the weaknesses of the algorithm and how these cons were improved upon through the AES algorithm. This paper will also include the future of both algorithms by comparing them to newer encryption models and techniques, touching on how the currently accepted AES model can continue to be adapted to the newly expected era of technology and cryptography.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".