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#botdetection — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #botdetection, aggregated by home.social.

  1. University of Missouri: User-friendly bot detection. “Traditional bot detection methods like CAPTCHA are effective, but they come at the cost of user experience. Our team wanted to explore whether machine learning could handle the same job entirely in the background — no interruptions, no puzzles, just seamless verification. Beyond bot detection, we saw potential for this approach to extend […]

    https://rbfirehose.com/2026/05/22/university-of-missouri-user-friendly-bot-detection/
  2. StyloBot free day as I ran myself ragged trying to get it going in my free time (very little of which I HAD finishing up 2x contracts!).

    Biggest win is dropping the ONNX dependency.

    Earlier versions used ONNX embeddings as a shortcut: turn a client signature into a vector and compare it.

    It worked, but it was never quite the right abstraction. Embeddings are built for language. StyloBot’s inputs are behavioural structures.

    The new version defines that behavioural vector space directly. Requests, sessions, browsers, bots, scrapers, and odd clients are placed into a real StyloBot-native space. The system ships with archetype centroids, then adapts those centroids to the actual traffic it sees.

    So instead of asking a model what a client 'means', StyloBot learns what your traffic looks like.

    StyloBot is REALLY a conceptually unfolded ML model so it sort of trains itself on real traffic around centroids and updates as it goes. It's ODD.

    Now out in Release Candidate github.com/scottgal/stylobot/r

    Plan is still for full release June 1st but the FOSS client MAY reach RTM quality before that (lots of manual testing!)

  3. StyloBot free day as I ran myself ragged trying to get it going in my free time (very little of which I HAD finishing up 2x contracts!).

    Biggest win is dropping the ONNX dependency.

    Earlier versions used ONNX embeddings as a shortcut: turn a client signature into a vector and compare it.

    It worked, but it was never quite the right abstraction. Embeddings are built for language. StyloBot’s inputs are behavioural structures.

    The new version defines that behavioural vector space directly. Requests, sessions, browsers, bots, scrapers, and odd clients are placed into a real StyloBot-native space. The system ships with archetype centroids, then adapts those centroids to the actual traffic it sees.

    So instead of asking a model what a client 'means', StyloBot learns what your traffic looks like.

    StyloBot is REALLY a conceptually unfolded ML model so it sort of trains itself on real traffic around centroids and updates as it goes. It's ODD.

    Now out in Release Candidate github.com/scottgal/stylobot/r

    Plan is still for full release June 1st but the FOSS client MAY reach RTM quality before that (lots of manual testing!)

    #BotDetection #CyberSecurity #DotNet #SQLiteVec #VectorSearch #BehaviouralInference #AIInfrastructure #OpenSource

  4. StyloBot free day as I ran myself ragged trying to get it going in my free time (very little of which I HAD finishing up 2x contracts!).

    Biggest win is dropping the ONNX dependency.

    Earlier versions used ONNX embeddings as a shortcut: turn a client signature into a vector and compare it.

    It worked, but it was never quite the right abstraction. Embeddings are built for language. StyloBot’s inputs are behavioural structures.

    The new version defines that behavioural vector space directly. Requests, sessions, browsers, bots, scrapers, and odd clients are placed into a real StyloBot-native space. The system ships with archetype centroids, then adapts those centroids to the actual traffic it sees.

    So instead of asking a model what a client 'means', StyloBot learns what your traffic looks like.

    StyloBot is REALLY a conceptually unfolded ML model so it sort of trains itself on real traffic around centroids and updates as it goes. It's ODD.

    Now out in Release Candidate github.com/scottgal/stylobot/r

    Plan is still for full release June 1st but the FOSS client MAY reach RTM quality before that (lots of manual testing!)

    #BotDetection #CyberSecurity #DotNet #SQLiteVec #VectorSearch #BehaviouralInference #AIInfrastructure #OpenSource

  5. StyloBot free day as I ran myself ragged trying to get it going in my free time (very little of which I HAD finishing up 2x contracts!).

    Biggest win is dropping the ONNX dependency.

    Earlier versions used ONNX embeddings as a shortcut: turn a client signature into a vector and compare it.

    It worked, but it was never quite the right abstraction. Embeddings are built for language. StyloBot’s inputs are behavioural structures.

    The new version defines that behavioural vector space directly. Requests, sessions, browsers, bots, scrapers, and odd clients are placed into a real StyloBot-native space. The system ships with archetype centroids, then adapts those centroids to the actual traffic it sees.

    So instead of asking a model what a client 'means', StyloBot learns what your traffic looks like.

    StyloBot is REALLY a conceptually unfolded ML model so it sort of trains itself on real traffic around centroids and updates as it goes. It's ODD.

    Now out in Release Candidate github.com/scottgal/stylobot/r

    Plan is still for full release June 1st but the FOSS client MAY reach RTM quality before that (lots of manual testing!)

    #BotDetection #CyberSecurity #DotNet #SQLiteVec #VectorSearch #BehaviouralInference #AIInfrastructure #OpenSource

  6. StyloBot free day as I ran myself ragged trying to get it going in my free time (very little of which I HAD finishing up 2x contracts!).

    Biggest win is dropping the ONNX dependency.

    Earlier versions used ONNX embeddings as a shortcut: turn a client signature into a vector and compare it.

    It worked, but it was never quite the right abstraction. Embeddings are built for language. StyloBot’s inputs are behavioural structures.

    The new version defines that behavioural vector space directly. Requests, sessions, browsers, bots, scrapers, and odd clients are placed into a real StyloBot-native space. The system ships with archetype centroids, then adapts those centroids to the actual traffic it sees.

    So instead of asking a model what a client 'means', StyloBot learns what your traffic looks like.

    StyloBot is REALLY a conceptually unfolded ML model so it sort of trains itself on real traffic around centroids and updates as it goes. It's ODD.

    Now out in Release Candidate github.com/scottgal/stylobot/r

    Plan is still for full release June 1st but the FOSS client MAY reach RTM quality before that (lots of manual testing!)

    #BotDetection #CyberSecurity #DotNet #SQLiteVec #VectorSearch #BehaviouralInference #AIInfrastructure #OpenSource

  7. New article about StyloBot - my FREE realtime adaptive automation blocker and detector.

    mostlylucid.net/blog/stylobot-

    Trying to put together a *human* level descriptor as I prepare to make it my full time gig (amongst other self-released projects).

    #botdetection #fraud #stylobot

  8. Getting stuck in an infinite “please try again” loop while creating a Microsoft account.
    It surely does a decent job blocking batch registrations too, right?

    #Microsoft #AccountSecurity #SpamPrevention #BotDetection #Tech

  9. AI-Driven Ad Fraud Detection | From Bot Identification to Real-Time Protection

    tuvoc.com/blog/ai-driven-ad-fr

    Learn how AI-powered ad fraud detection identifies bots and suspicious traffic in real time. Discover how it helps prevent fraud and safeguard ad revenue effectively.

    #AdFraud
    #AIinAdvertising
    #FraudDetection
    #AdTech
    #ProgrammaticAdvertising
    #BotDetection
    #AdSecurity
    #MachineLearning
    #DigitalAdvertising

  10. AI-Driven Ad Fraud Detection | From Bot Identification to Real-Time Protection

    tuvoc.com/blog/ai-driven-ad-fr

    Learn how AI-powered ad fraud detection identifies bots and suspicious traffic in real time. Discover how it helps prevent fraud and safeguard ad revenue effectively.

    #AdFraud
    #AIinAdvertising
    #FraudDetection
    #AdTech
    #ProgrammaticAdvertising
    #BotDetection
    #AdSecurity
    #MachineLearning
    #DigitalAdvertising

  11. AI-Driven Ad Fraud Detection | From Bot Identification to Real-Time Protection

    tuvoc.com/blog/ai-driven-ad-fr

    Learn how AI-powered ad fraud detection identifies bots and suspicious traffic in real time. Discover how it helps prevent fraud and safeguard ad revenue effectively.

    #AdFraud
    #AIinAdvertising
    #FraudDetection
    #AdTech
    #ProgrammaticAdvertising
    #BotDetection
    #AdSecurity
    #MachineLearning
    #DigitalAdvertising

  12. AI-Driven Ad Fraud Detection | From Bot Identification to Real-Time Protection

    tuvoc.com/blog/ai-driven-ad-fr

    Learn how AI-powered ad fraud detection identifies bots and suspicious traffic in real time. Discover how it helps prevent fraud and safeguard ad revenue effectively.

    #AdFraud
    #AIinAdvertising
    #FraudDetection
    #AdTech
    #ProgrammaticAdvertising
    #BotDetection
    #AdSecurity
    #MachineLearning
    #DigitalAdvertising

  13. AI-Driven Ad Fraud Detection | From Bot Identification to Real-Time Protection

    tuvoc.com/blog/ai-driven-ad-fr

    Learn how AI-powered ad fraud detection identifies bots and suspicious traffic in real time. Discover how it helps prevent fraud and safeguard ad revenue effectively.

    #AdFraud
    #AIinAdvertising
    #FraudDetection
    #AdTech
    #ProgrammaticAdvertising
    #BotDetection
    #AdSecurity
    #MachineLearning
    #DigitalAdvertising

  14. 😆 Ah, The Register, where cutting-edge #journalism meets the 403 Forbidden error. 🇺🇸💼 Apparently, the US is threatening extra tariffs on countries that dare to regulate their tech overlords, but good luck reading about it while battling bot detection! 📴🔒
    theregister.com/2025/08/26/tru #TheRegister #TechTariffs #BotDetection #403Forbidden #HackerNews #ngated

  15. 🕵️‍♂️🔍 A noble quest to decode the mysteries of a bot detection script, because, you know, #privacy matters, or something. The author's Herculean effort to untangle #JavaScript spaghetti would be impressive if it weren't so pointlessly convoluted. 🧩🤦‍♂️ Just remember, kids: everything's a string until you need it to be an integer!
    nullpt.rs/reversing-botid #botDetection #codingMysteries #programmingHumor #developerLife #HackerNews #ngated

  16. 🤖 So, apparently, we're creating an "invisible" Turing Test now—because regular visibility is just too mainstream 🤦‍♂️. 🤔 Google's reCAPTCHA v3 is basically the Sherlock Holmes of bot detection... except when it's not. But hey, if you want to play detective, check out sections 2 and 3 for some thrilling mouse movement drama! 🎭
    research.roundtable.ai/proof-o #invisibleTuringTest #GoogleReCAPTCHA #botDetection #mouseMovementDrama #techHumor #HackerNews #ngated

  17. 🤖 #AppTrana #Bot Management Update 🤖

    With AppTrana's latest #botmanagement enhancements, users can now define what a bot means for their application's unique context and establish custom mitigation actions based on the user's behaviour.

    Learn more about this enhancement here: bit.ly/3zt6fXy

    #botpolicies #webapplications #botdetection #falsepositives #botattacks #bots #botprotection #scraperbots #botmitigation #indusface

  18. 🤖 #AppTrana #Bot Management Update 🤖

    With AppTrana's latest #botmanagement enhancements, users can now define what a bot means for their application's unique context and establish custom mitigation actions based on the user's behaviour.

    Learn more about this enhancement here: bit.ly/3zt6fXy

    #botpolicies #webapplications #botdetection #falsepositives #botattacks #bots #botprotection #scraperbots #botmitigation #indusface

  19. 🤖 #AppTrana #Bot Management Update 🤖

    With AppTrana's latest #botmanagement enhancements, users can now define what a bot means for their application's unique context and establish custom mitigation actions based on the user's behaviour.

    Learn more about this enhancement here: bit.ly/3zt6fXy

    #botpolicies #webapplications #botdetection #falsepositives #botattacks #bots #botprotection #scraperbots #botmitigation #indusface

  20. Today, I visited #Twitter for the first time in a month or two. As per usual, I had a few new followers. I went to look at their likes but couldn't; I only now consciously realize likes were my reliable bot test. A normal person will have like clusters — 3 or more in a row with commonalities: topic, tone, media, same user, language, etc. The bot likes were random and often included garbage posts.

    Is that why they removed likes? Prob not, but...

    #BotDetection

  21. Well, I didn't buy this band shirt because I just liked the logo. 😆

    Go ahead system, test me... Bring it on. 🤘

    #BotDetection #website #eCommerce