> For the complete documentation index, see [llms.txt](https://lus-organization.gitbook.io/lu_paper_reading/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://lus-organization.gitbook.io/lu_paper_reading/security/darknet/sec-24-does-online-anonymous-market-vendor-reputation-matter.md).

# Does Online Anonymous Market Vendor Reputation Matter?

**Link:** <https://www.usenix.org/conference/usenixsecurity24/presentation/cuevas>

**Conference:** Usenix Security 2024 **Keywords:** : online anonymous marketplace (OAM), social media

### Summary

Reputation is crucial for trust in underground markets but it lacks credit system for measuring their reputation. This work use **(i) longevity** and **(ii) future financial success** to measure their success on **8** OAM from 2011 to 2023. The findings provide empirical insights into early identification of potential high-scale vendors, effectiveness of "reputation poisoning" strategies, and how reputation systems could contribute to harm reduction in OAMs.

### Contributions

1. We **quantify the impact of various market and forum-derived features** on vendor longevity and find that feedback scores (including imported product reviews from other markets) have a significant impact on increasing longevity across most markets we study;
2. We find that (both positive and negative) **reputation signals** from forums explain vendor survivability, but overall have little predictive power for vendor success;
3. We demonstrate we can build a **generalizable model to predict**, more accurately than raw feedback, which vendors may leave the market in the short-term (1–3 months);
4. We find that future financial success is **predictable**, particularly for the top/bottom 25% of vendors, and even on previously unseen markets.
5. We find that **features external to the market, and time-series representations of features** not only fail to increase the predictive power, but instead often **decrease** it.

### Data

1. Merketplace: Opensource data + crawler
2. Reddit: /r/HansaDarknet-Market (Sep 2015 - Sep 2017, 264 posts, 3613 comments), /r/DarkNetMarkets (125,300 posts, 1,850,533 comments, October 2013 - September 2017)
3. Nemesis Forum (reddit-like Nemesis darknet forum): 4,018 posts and 12,710 comments from March 2022 to February 2023

### Feature

1. Revenue, feedback, and listing
2. Temporal features
3. Forum features
4. Listing category

### Survivability Drivers

Average feedback value (FB) Presence in other markets (POM) Category A drugs (MA), category B drugs (MB), and digital goods (MD). Wealth tier

![alt text](https://169318583-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FUAYVuGJIcqGBOPcOsA14%2Fuploads%2Fgit-blob-e76d5c5d3277e44ceb05ab0b3576c23eaa588c89%2Fimage-1.png?alt=media) ![alt text](https://169318583-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FUAYVuGJIcqGBOPcOsA14%2Fuploads%2Fgit-blob-35c257f3273ff4c615f6711eea0623ca0a1c2729%2Fimage-2.png?alt=media)

#### Results

1. Reputation and cross-market make vendor more survivable
2. Even a little bit **Reputation Slander Attack** could work (with calculating a case cost, *'It would take 254 1-star reviews for a total cost of ∼ $2, 286 to reduce their average rating by 1 unit and thus increase their (predicted) hazard by 64%'*)

### Future Financial Success Prediction

Random Forest, Time Series Forest classifier

![alt text](https://169318583-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FUAYVuGJIcqGBOPcOsA14%2Fuploads%2Fgit-blob-2d2ff275e4022b9254136819c132f7fec92603a4%2Fimage-3.png?alt=media)

### Vendor Disapperance Prediction

Random Forest, Time Series Forest classifier ![alt text](https://169318583-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FUAYVuGJIcqGBOPcOsA14%2Fuploads%2Fgit-blob-f2b30dbe47c85e297a75ee32b62e34e69448cf88%2Fimage-4.png?alt=media)

### Conclusion

Correlation between reputation and OAM

### Pros:

1. Crossplatform
2. Interesting perspective (especially the reputation poisoning with fake bad reviews)