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Real-Time GPS Spoofing and Jamming Detection Using Airplanes (ADS-B)

Hacker News

Real-Time GPS Spoofing and Jamming Detection Using Airplanes (ADS-B)

Hey HN, Over the past few months, we have been building a tool that uses live ADS-B data (messages broadcasted by airplanes) from the OpenSky Network to track GPS spoofing in real time. We see that over 1,000 flights daily are impacted, with planes jumping to spoofed locations. We also track GPS jamming with a short delay (about 2 hours). The challenge wasn’t just detecting spoofing, it was figuring out where it started (the jamming makes it particularly difficult). We would love to get your feedback or hear any suggestions for features you’d like to see!

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: 000, io · Missing: https docs, excited, just released
65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: using, open · Missing: mac, agents, macos
55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real time · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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