Proceedings of the 2018 Workshop on Attacks and Solutions in Hardware Security
Bibliographic record
Abstract
It is our great pleasure to welcome you to the Second Workshop on Attacks and Solutions in Hardware Security 2018 (ASHES 2018), a post-conference satellite workshop of the ACM Conference on Computer and Communications Security 2018 (CCS 2018) in Toronto, Canada! ASHES deals with all aspects of hardware security, and welcomes any contributions to this area. Besides being a forum for mainstream hardware security research, its mission is to specifically foster new concepts, solutions, and methodological approaches, and to promote new application scenarios. This includes, for example, new attack vectors on secure hardware, the merger of nanotechnology and hardware security, novel designs and materials, lightweight security hardware, and physical unclonable functions (PUFs) on the methodological side, as well as the internet of things, automotive security, smart homes, supply chain security, pervasive and wearable computing on the applications side. ASHES thereby aims at giving researchers and practitioners a unique opportunity to share their perspectives with others on various emerging aspects of hardware security research. In order to account for hardware security as a rapidly developing discipline, ASHES routinely offers four categories of submission: Full papers; Short papers;Systematization of Knowledge (SoK) papers, which structure or survey a certain subarea within hardware security; Wild and Crazy (WaC) papers, whose aim is to distribute a promising and potentially seminal research idea at an early stage to the community. Our call for papers this year attracted 30 submissions overall, of which 27 were conforming to submission and formatting requirements. This marks an increase of 50 percent compared to last year, where ASHES 2017 had received 20 submissions. Two submissions fell into the wild-andcrazy paper category; one into the systematization of knowledge category; the rest were regular full and short papers. Geographically, the different co-authors of submissions this year were associated with institutions in the US (18), closely followed by Europe (13), and India (1).
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.112 | 0.045 |
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".