A Software-Defined Deterministic Internet of Things (IoT) with Artificial Intelligence (AI) for Quantum-Safe Cyber-Security
Bibliographic record
Abstract
The next-generation Internet of Things (IoT) will enable Industry 4.0 and Smart Cyber-Physical Systems, including Smart Cities and Smart Manufacturing. These Smart Systems require: (i) ultra-low latencies, and (ii) immunity from cyber-attacks. This paper explores a “Software-Defined Deterministic IoT”, with Artificial Intelligence (AI) for Cyber-Security. It introduces a new sub-layer (3a) of “Software Defined Wide Area Networks (SD-WANs)”, using simple and secure deterministic packet switches (ie low-cost FPGAs). A “Software Defined Networking” (SDN) control-plane uses collaborative AI systems to implement Zero Trust Architectures (ZTAs) and Guaranteed Intrusion Detection Systems (IDSs), to control access to all critical resources. The SD-WANs can support millions of Deterministic Virtual Private Networks (DVPNs).The approach has many benefits: (i) All interference, congestion, and Distributed Denial-of-Service (DDOS) attacks are eliminated; (ii) End-to-end delays are determined by the speed of light in fiber; (iii) The SD-WANs provide hardware support for the US NIST ZTA and Post Quantum Cryptography (PQC); (iv) All communications within a DVPN are encrypted with PQC, and are immune to attacks from Quantum Computers; (v) The expected number of a successful cyber-attacks per year against a nation’s critical infrastructure from external cyber-attackers is zero, when using Quantum-Safe ciphers; (vi) Total cost savings are estimated at $100s of Billions USD per year.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".