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Generate Mitre ATT&CK from a List of CVE

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Generate Mitre ATT&CK from a List of CVE

CVE2CAPEC is a free tool to generate a MITRE ATT&CK Navigator from a list of CVE. MITRE ATT&CK is a framework that you can use to see the links between multiple security findings (here vulnerabilities from the CVE database). MITRE ATT&CK represents the path an attacker could use on your information system, aka "KillChain". CVE2CAPEC is built on an open source repo https://github.com/Galeax/CVE2CAPEC/ with JSON data about CVE, CWE, CAPEC, and MITRE ATTACK Techniques, that you can use for your own projects as well. Feel free to play with it and to open any issue if your need other features!

Visit productView on Hacker NewsOct 25, 2024
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Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released
51%51% 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: open · Missing: mac, agents, macos
49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
40%40% predicted probability of success on Indie Hackers, 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.

Incorrect prediction on native model

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