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Source channel @githubtrending · Post #15601 · Apr 5

#yara#awesome_list#blueteam#blueteam_tools#cti#detection#detection_engineering#dfir#hacktools#incident_response#ioc#iocs#ir#ransomware#redteam#rmm#security#siem#soc#threat_hunting#threat_intelligence You can access comprehensive security detection lists and threat hunting resources that help identify malicious activity across your infrastructure. These curated collections include indicators like suspicious file hashes, domain names, IP addresses, and behavioral patterns organized by threat type—from ransomware and phishing to command-and-control servers and vulnerable drivers. By integrating these lists into your security tools like SIEM platforms and endpoint detection systems, you gain immediate visibility into known threats while learning detection methodologies through guides and YARA rules. This accelerates your ability to hunt for compromises, validate security controls, and stay current with emerging attack techniques without building detection logic from scratch. https://github.com/mthcht/awesome-lists

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djangoproject

@djangoproject · Post #274 · 03/18/2017, 01:48 AM

https://github.com/riga/tfdeploy Google's TensorFlow framework is taking off big-time now that it's at a full 1.0 release. One common question about it: How can I make use of the models I train in TensorFlow without using TensorFlow itself? #Tfdeploy is a partial answer to that question. It exports a trained TensorFlow model to "a simple #NumPy-based callable," meaning the model can be used in Python with Tfdeploy and the the NumPy math-and-stats library as the only dependencies. Most of the operations you can perform in TensorFlow can also be performed in Tfdeploy, and you can extend the behaviors of the library by way of standard Python metaphors (such as overloading a class). Now the bad news: Tfdeploy doesn't support GPU acceleration, if only because NumPy doesn't do that. Tfdeploy's creator suggests using the gNumPy project as a possible replacement. #Machine_learning