The “Cyber Security via Determinism” Paradigm for a Quantum Safe Zero Trust Deterministic Internet of Things (IoT)
Bibliographic record
Abstract
The next-generationInternet of Things(IoT) will control the critical infrastructure of the 21st century, including theSmart Power GridandSmart Cities. It will also supportDeterministic Communications, where ‘deterministic traffic flows’ (D-flows) receive strictQuality-of-Service(QoS) guarantees. A‘Cybersecurity via Determinism’paradigm for the next-generation‘Industrial and Tactile Deterministic IoT’is presented. A forwarding sub-layer of simple and secure ‘deterministic packet switches’ (D-switches) is introduced into layer-3. This sub-layer supports many deterministicSoftware Defined Wide Area Networks(SD-WANs), along with 3 new tools for improving cyber security:Access Control,Rate Control, andIsolation Control. ASoftware Defined Networking(SDN) control-plane configures each D-switch (ie FPGA) with multiple deterministic schedules to support D-flows. The SDN control-plane can embed millions of isolatedDeterministic Virtual Private Networks(DVPNs) into layer 3. This paradigm offers several benefits: 1) All congestion, interference, andDistributed Denial-of-Service(DDOS) attacks are removed; 2) Buffer sizes in D-switches are reduced by 1000+ times; 3) End-to-end IoT delays can be reduced to ultra-low latencies, i.e., the speed-of-light in fiber; 4) The D-switches do not require Gigabytes of memory to store large IP routing tables; 5) Hardware support is provided in layer 3 for the US NISTZero Trust Architecture; 6) Packets within a DVPN can be entirely encrypted usingQuantum Safeencryption, which is impervious to attacks byQuantum Computersusing existing quantum algorithms; 7) The probability of an undetected cyberattack targeting a DVPN can be made arbitrarily small by using longQuantum Safeencryption keys; and 8) Savings can reach$\$ $10s of Billions per year, through reduced capital, energy and operational costs.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".