Analysis of two days of leaked censorship log files from Syria shows that 1.57% of the population accessed at least one censored site — a proportion the authors argue is far too large for a user-focused surveillance system to pursue individually. This implies that simply flagging all users who access censored content is not a feasible targeting strategy for surveillance.

Beverly et al. found that 77% of Internet clients can spoof source addresses within their own /24 and 11% can spoof within their own /16, with these characteristics holding across a wide range of countries and regions. The authors use this result to argue that IP-spoofed cover traffic — where measurement probes appear to originate from many hosts in the same AS — is broadly feasible in practice.

§4.2 defense

The authors argue that it is almost certainly impossible to eliminate — or even definitively quantify — the risk to users who perform censorship measurements, because surveillance system capabilities are rapidly evolving and in some cases unknowable; retribution in adversarial environments may not follow due process. The paper explicitly states that its techniques had not been deployed on real networks as of writing because "a better consideration of the associated risks is warranted."

§6–§7 policy

Spam-cloaked censorship measurements were correctly classified as spam by Proofpoint (the authors' university spam filter), validating surveillance evasion; separately, MX queries sent from a PlanetLab node in China confirmed that the GFW injected bad A DNS responses for both A-record and MX-record lookups for twitter.com and youtube.com, validating measurement accuracy.

§3.2.3 evaluation

Surveillance systems are fundamentally more selective than censorship systems due to storage constraints: as of 2009 the NSA could store only 7.5% of received traffic across 592 tapped 10 Gbps links with only 69 10 Gbps backhaul links, and the authors' campus network retains non-alert metadata for ~36 hours and IDS alerts for ~1 year. Censorship systems by contrast are transaction-focused and retain only enough data to process real-time requests. This asymmetry creates an exploitable gap: traffic that does not stand out from the population is discarded before reaching human analysts.

§2.1–§2.2 evaluation

The largest single source of censored domains in the GNL is MESA lab's SNI monitoring dataset (E21-SNI-Top200w.txt) containing 57,362 censored domains, and E21-SNI-Top120W-20221020.txt with 36,467 domains—totaling over 93K domains from network tap data alone for a single country (E21 = Ethiopia per InterSecLab attribution). A separate Xinjiang dataset (XJ-CUCC-SNI-Top200w.txt) contains 13,604 domains. These datasets "do not seem to come from popular domain lists, and instead appear to be gathered from network taps," confirming that Geedge builds censorship target lists directly from passive traffic observation.

2026-sheffey-geedge §4.2, Table 3 dpisni-blockingmeasurement-platform

Of 6,915,266 domains extracted from the 572 GiB Geedge Networks Leak (GNL), 298,955 censored domains (93.7% of all GNL-censored domains) appear in neither Tranco top-1M nor CitizenLab test lists. Measurements across China (Guangzhou/Nanjing), Myanmar, Pakistan, and Algeria confirmed censorship via DNS injection and SNI-based TLS connection termination. The GNL covers 25–62% of Tranco-censored domains across countries, showing substantial but incomplete overlap. This vendor-side ground truth reveals a censorship surface roughly two orders of magnitude larger than curated academic test lists.

2026-sheffey-geedge §4.1, Table 2 dpisni-blockingdns-poisoningmeasurement-platform

CensorAlert aggregates censorship signals from heterogeneous open sources — including OONI, Cloudflare Radar, NetBlocks, GitHub Issues of circumvention tools (Hysteria, Xray), Net4People BBS, NTC Party forum, Mastodon, X, Telegram channels, and arXiv — normalizing each item into a common schema preserving timestamp, source, raw data, and provenance with a link to the original content.

2026-zohaib-extended §2 (Data Sources) measurement-platform

Systematic measurement platforms (OONI, Censored Planet, Cloudflare Radar, NetBlocks) have inherent blind spots due to geographic coverage and protocol-specific test constraints; critical censorship discoveries — including GFW fully-encrypted protocol blocking and regional Chinese censorship — were first surfaced by user reports on forums and GitHub issue pages of circumvention tools, not by automated measurement infrastructure.

2026-zohaib-extended §1 measurement-platformfully-encrypted-detect

CensorAlert generates text embeddings (OpenAI text-embedding-3-small) from each item's summary, title, and tags to cluster semantically similar reports within configurable time windows; near-duplicates such as reposts, copied headlines, and translations are collapsed into a single canonical post that preserves all original source URLs and metadata.

2026-zohaib-extended §2 (AI-based Scoring) measurement-platform

CensorAlert's LLM agent scores each ingested item 0–5 on five independent dimensions — credibility, novelty, impact, timeliness, and verifiability — then computes a normalized significance score (0–10) from these components; items are processed every two hours using OpenAI GPT-5 Thinking (hosted on Azure) constrained to return structured JSON output.

2026-zohaib-extended §2 (AI-based Scoring) measurement-platform