Vertical IP Protection of the Next-Generation Devices: Quo Vadis?
Bibliographic record
Abstract
With the advent of 5G and IoT applications, there is a greater thrust in terms of hardware security due to imminent risks caused by high amount of intercommunication between various subsystems. Security gaps in integrated circuits, thus represent high risks for both-the manufacturers and the users of electronic systems. Particularly in the domain of Intellectual Property (IP) protection, there is an urgent need to devise security measures at all levels of abstraction so that we can be one step ahead of any kind of adversarial attacks. This work presents IP protection measures from multiple perspectives-from system-level down to device-level security measures, from discussing various attack methods such as reverse engineering and hardware Trojan insertions to proposing new-age protection measures such as multi-valued logic locking and secure information flow tracking. This special session will give a holistic overview at the current state-of-the-art measures and how well we are prepared for the next generation circuits and systems.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.007 |
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".