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Didsomeoneclone.me – a free tool to detect malicious website clones

Hacker News

Didsomeoneclone.me – a free tool to detect malicious website clones

Hi, As you might know cybercriminals are cloning websites to perform phishing attacks, to scam visitors, obtain credentials or distribute malware. Im a security researcher and Ive been investigating many phishing sites. During this work I saw the lack of protection mechanisms against website cloning. While some techniques are available. Due to this I decided to create a free tool, that is easy to use for everyone. The tool detects whenever a website is cloned. A notification is sent by email whenever a clone is detected. This allows a website owner to take necessary steps against the phishing site. The website of my tool for more information: https://didsomeoneclone.me/ If you are interested how it was technically build, you can read this blog: https://forsec.nl/Building-did-someone-clone-me/ Feedback is welcome!

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
45%45% 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 HuntUnlikely to reach the leaderboard · Strong signals: email · Missing: mac, agents, macos
37%37% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
36%36% 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
11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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