Search Results for "malware-patch"
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Showing 3 open source projects for "malware-patch"
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Automate contact and company data extractionGenerate leads at scale without building or maintaining scrapers. Use 10,000+ ready-made tools that handle authentication, pagination, and anti-bot protection. Pull data from business directories, social profiles, and public sources, then export to your CRM or database via API. Schedule recurring extractions, enrich existing datasets, and integrate with your workflows.
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ThinkReview
AI-powered code reviews for GitLab & Azure DevOps. Zero setup. Powered
...The extension Summarises merge requests , provided suggestions and find potential security issues , provide smart questions to dive deeper and chat with your pull requests The extension works out of the box without any complex setup - Supports custom Self hosted Gitlab as well as gitlab.com and gitlab enterprise Enhance your GitLab MRs and Azure Devops PRsworkflows with automatic patch diff access and AI-powered code reviews. Key Features: • Auto-detect MR and PR Pages: The extension automatically recognizes when you're viewing a GitLab merge request. • Works with any instance of gitlab and azure devops whether it's on a custom domain or on gitlab.com • Integrated AI Code Review: Get instant AI-powered code reviews displayed directly in the GitLab interface... -
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Malware Analysis Network in Taiwan
MAN in Taiwan, MiT
Malware Analysis Network in Taiwan <Man in Taiwan, MiT> Welcome to contact us (TonTon@TWMAN.ORG) if you are interested in collaborating with us. This project is open source and distributed under the GNU General Public License version 3. Please feel free to add to or modify this source and propose changes or new converters. Developer & Copyrighted by : TonTon Hsien-De Huang Prompter: Jazz Yao-Tsung Wang, Figaro Chen-Ho Yang | Logo Desinger:Temaki Guo Community on Google+：http://X.TWMAN.ORG/Community/ SourceForge: https://sourceforge.net/projects/twmanplus/files/ FaceBook: https://www.facebook.com/TWMAN.PLUS -
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A packet dissector driven by machine learning algorithms. You train it to recognize specific types of packets by showing it examples and counterexamples of some packet type, and it will figure out which bits in the packet define it as the type you seek.
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