"GitHub Curates Comprehensive List of AI Tools, Papers, and Datasets for Cybersecurity Applications"

GitHub - AmanPriyanshu/Awesome-AI-For-Security: A Curated List of Tools, Papers, and Datasets for Applying AI to Cybersecurity Tasks This repository, curated by Aman Priyanshu, is a comprehensive resource hub for those interested in leveraging artificial intelligence (AI) in the realm of cybersecurity. The list primarily highlights modern AI technologies, including Large Language Models (LLMs), Agents, and Multi-Modal systems, and explores their practical applications in security operations. Contents Related Awesome Lists In addition to this list, Aman has compiled references to other collections and resources that might be of interest to cybersecurity enthusiasts and professionals. These related lists can help broaden your understanding and provide additional insights into various aspects of AI and security. Models This section features AI models specifically tailored for security applications and scenarios. From advanced neural networks to highly optimized machine learning algorithms, these models are designed to address a wide range of cybersecurity challenges, such as detecting malware, identifying vulnerabilities, and enhancing threat intelligence. Specialized Security Models Sub-sections under "Models" delve deeper into specific types of models used in cybersecurity. For instance, you'll find detailed information about models that focus on network security, endpoint protection, and behavioral analysis. Each entry includes links to relevant papers and repositories, making it easy to explore the underlying technology and its implementation. Datasets AI systems require high-quality data for training and fine-tuning. The "Datasets" section provides a collection of resources designed for security-related tasks. These datasets are essential for developing robust AI models that can accurately identify threats and mitigate risks. Pre-Training Datasets: These are general-purpose datasets used to pre-train AI models before they are fine-tuned on security-specific tasks. They help establish a strong foundational understanding before the model is exposed to more specific data. IFT & Capability Datasets: Intended for in-field training and evaluation, these datasets focus on real-world scenarios and capabilities, ensuring that AI models can perform effectively in actual cybersecurity environments. Benchmarks & Evaluation Evaluating the performance of AI systems in cybersecurity contexts is crucial for ensuring their reliability and effectiveness. The "Benchmarks & Evaluation" section covers frameworks and methodologies for assessing AI systems' capabilities in areas such as vulnerability assessment, threat intelligence, offensive security, and general security knowledge. Vulnerability Assessment: Tools and benchmarks for identifying and assessing software and system vulnerabilities using AI. Threat Intelligence: Resources for improving the accuracy and efficiency of threat detection and analysis through AI-driven methods. Offensive Security: Methods and frameworks for testing AI's capabilities in offensive security operations, such as penetration testing and malware development. General Security Knowledge: Comprehensive evaluations of AI systems' overall security knowledge and proficiency. Publications Stay up-to-date with the latest academic and industry research on AI applications in cybersecurity. The "Publications" section includes a curated selection of papers and articles that delve into the theoretical and practical aspects of using AI for security. Topics range from model development to dataset creation and benchmarking. Models & Datasets: Research focused on creating and optimizing AI models and datasets for security applications. Benchmarking & Evaluations: Studies on the effectiveness and reliability of different AI systems in various security contexts. Other: Miscellaneous publications that offer broader perspectives on AI and cybersecurity. Tools & Frameworks The "Tools & Frameworks" section is dedicated to software tools that implement AI for security applications. From adversarial machine learning tools to security testing frameworks, this section provides a comprehensive overview of the tools available to cybersecurity practitioners. Adversarial ML: Tools that help in understanding and defending against adversarial attacks on machine learning systems. Security Testing: Resources for conducting rigorous security tests using AI, including automated testing suites and vulnerability scanners. Learning Environments:Platforms that allow developers and researchers to experiment with AI in secure and controlled environments. Security Agents In this section, you'll find information on AI systems designed to perform security-related tasks with varying degrees of autonomy. These agents can automate mundane tasks, assist in complex threat analyses, and even take proactive measures to protect systems from emerging threats. Autonomous Agents: Fully autonomous AI systems that operate independently to monitor and secure networks. Red Team Agents: AI-driven agents used for ethical hacking and penetration testing, simulating real-world attacks to identify and remediate security weaknesses. Contribute Community contributions are highly valued and encouraged. If you have any new tools, papers, or datasets to add, please refer to the contribution guidelines provided in the repository. Your input can help keep this resource hub current and comprehensive. License This project is licensed under the CC0 license, which means that all content is freely available for use, modification, and distribution without attribution. By providing a well-organized and continuously updated repository, Aman Priyanshu aims to facilitate the adoption and advancement of AI in cybersecurity. Whether you're a researcher, developer, or security professional, this list offers a wealth of valuable resources to explore and utilize in your work.