Multi-point Security by a Multiplatform-compatible Multifunctional Authentication and Encryption Board
Bibliographic record
Abstract
Securing the access in networks is a first-order concern that only gains importance with the advent of Internet of Things (IoT). In this paper, a security system is presented for password-free access over the secured link. It makes the connection faster than manual authentication and facilitates Machine-to-Machine (M2M) secure interactions, as required for IoT. The authentication procedure includes the exchange of certificate and challenge/response pairs, which are stored and computed in an external security coprocessor. The system enforces the authentication protocol, includes error detection, and handles multiple devices according to their Operating Systems (OS) through their connections/ disconnections. It also performs encryption, if necessary. It is applicable on application level for devices, including IoT based devices, sensors, Android, and iOS-based smartphones. The devices that have the correct certificate and can solve the challenge can connect to the network linked with the security system. The system security is hardened because the sensitive authentication elements such as keys, certificates, and challenge responses are invisible to users and are exchanged only using strong hashing algorithms that are irreversible. The proposed hardware security system can augment any supporting network, converting the entire insecure network into a secured one, as well as retrofit existing insecure Bluetooth devices for secure access. The system incurs low overhead in time and energy by performing security operations in an ASIC coprocessor, and can be shared to secure access to multiple devices, which reduces both energy and cost.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".