跳到正文
cccskills
免费在 GitHub 公开

ccs-topic-selection

Use when deciding whether a security project fits ACM CCS versus IEEE S&P, USENIX Security, NDSS, PETS, or a crypto/theory venue, identifying the security contribution type, and sharpening the threat model and attacker capability before writing begins.

中文简介正在准备中。当前显示原文说明。

查看安装方法

包含的文件(1)

  • SKILL.md3.7 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

CCS Topic Selection

Use this before writing. ACM CCS is the SIGSAC flagship: it rewards work with a concrete attacker, a defensible threat model, and evidence that survives an adversarial program committee. Decide venue by community and contribution type, never by prestige ranking.

Fit test

  • Prefer CCS when the contribution is a broad computer-security result — a new attack class, a defense with measured cost, an applied-cryptography protocol, a systems or web-security mechanism, or a measurement study — aimed at the cross-area SIGSAC community.
  • Route to IEEE S&P (Oakland) when the work suits that PC's taste for foundational or systematization framing and the November cycle fits your calendar better.
  • Route to USENIX Security when the contribution is artifact-heavy systems security whose evidence lives in a runnable tool and open benchmark.
  • Route to NDSS when the core is network- and distributed-system security (protocols, DNS, routing, malware infrastructure).
  • Route to PETS/PoPETs when privacy is the primary lens rather than one property among many.
  • Route to CRYPTO/EUROCRYPT when the contribution is cryptographic theory whose proof, not its deployment, is the result.

Fit signal table

Signal in the projectCCS reading
New attack with a clearly bounded adversary and demonstrated impactCore fit — the house genre
Defense evaluated against adaptive attacks with deployment costCore fit
Applied crypto protocol with implementation and measured overheadCore fit
Internet-scale or ecosystem measurement with validated samplingCore fit
Pure cryptographic hardness proof, no systemCRYPTO/EUROCRYPT or a theory venue
Privacy-first metrics with no other security propertyPETS/PoPETs

Vignette: where a side-channel result goes

A project extracts keys from a deployed TLS library via a microarchitectural side channel, with a proof-of-concept exploit and a constant-time patch. CCS reading: strong fit — a concrete attacker, measured leakage, and a defense with overhead numbers is exactly the CCS arc. Strip the exploit and keep only an abstract leakage bound, and it drifts toward a crypto theory venue; expand the network-measurement of vulnerable hosts into the whole story, and NDSS becomes plausible; foreground only the privacy harm to users, and PETS fits better.

Sharpening moves before committing

  • Name the attacker: capabilities, knowledge, position, and what success means. If you cannot write the threat model in three sentences, the contribution is not yet CCS-shaped.
  • Decide the contribution type — attack, defense, protocol, measurement, tool, or study — because reviewers grade each against a different evidence bar.
  • Confirm the result fits the 12-page ACM sigconf body; CCS bodies are dense, and a paper needing 30 pages of proofs may belong at a journal or a theory venue.
  • Scope drifts across cycles; scan the current CFP topic list and recent proceedings before final routing.

Output format

[Fit] strong CCS / possible CCS / better elsewhere
[Best venue] CCS / IEEE S&P / USENIX Security / NDSS / PETS / crypto venue / other
[Contribution type] attack / defense / protocol / measurement / tool / study
[Threat model in one line] <adversary capability and goal>
[Top rejection risk] <threat-model / novelty / evidence / ethics / scope>
[Next action] <sharpen threat model, add evidence, reframe, or switch venue>

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.

中文简介正在准备中。当前显示原文说明。

brycewang-stanford/Awesome-Journal-Skills12942026年9月27日 更新

Use when drafting an AAAI author response (rebuttal) under the single short character-limited author-feedback window, the no-URL rule, no-new-results guidance, AI-generated-review handling, and the AAAI two-phase review process where Phase-2 papers receive one feedback round before SPC/AC discussion.

中文简介正在准备中。当前显示原文说明。

brycewang-stanford/Awesome-Journal-Skills12942026年9月27日 更新

Use when preparing an accepted AAAI paper for camera-ready source submission to AAAI Press, including proceedings page limits, two-column template compliance, copyright transfer, purchased extra technical pages, deanonymization, registration, oral or poster presentation, and final public artifact release.

中文简介正在准备中。当前显示原文说明。

brycewang-stanford/Awesome-Journal-Skills12942026年9月27日 更新

Use when designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and reproducibility-checklist alignment for Phase-1 survival.

中文简介正在准备中。当前显示原文说明。

brycewang-stanford/Awesome-Journal-Skills12942026年9月27日 更新

Use when positioning an AAAI paper's novelty against archival work, contemporaneous arXiv or workshop papers, and AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors across the broad AI scope, while staying inside AAAI's dual-submission and AI-as-source policy constraints and writing a related-work section legible to non-specialist reviewers.

中文简介正在准备中。当前显示原文说明。

brycewang-stanford/Awesome-Journal-Skills12942026年9月27日 更新

Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that Phase-1 reviewers use to judge rigor across AAAI's broad AI scope.

中文简介正在准备中。当前显示原文说明。

brycewang-stanford/Awesome-Journal-Skills12942026年9月27日 更新

查看 brycewang-stanford 的全部技能

报告此技能的问题