Application Of Large Language Models Llms For Software Vulnerability Detection
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Application of Large Language Models (LLMs) for Software Vulnerability Detection
Large Language Models (LLMs) are redefining the landscape of cybersecurity, offering innovative methods for detecting software vulnerabilities. By applying advanced AI techniques to identify and predict weaknesses in software code, including zero-day exploits and complex malware, LLMs provide a proactive approach to securing digital environments. This integration of AI and cybersecurity presents new possibilities for enhancing software security measures. Application of Large Language Models (LLMs) for Software Vulnerability Detection offers a comprehensive exploration of this groundbreaking field. These chapters are designed to bridge the gap between AI research and practical application in cybersecurity, in order to provide valuable insights for researchers, AI specialists, software developers, and industry professionals. Through real-world examples and actionable strategies, the publication will drive innovation in vulnerability detection and set new standards for leveraging AI in cybersecurity.
Leveraging Large Language Models for Quantum-Aware Cybersecurity
As the digital landscape evolves, the growing threat of cyberattacks has prompted the need for more advanced security measures. One of the most promising developments in cybersecurity is the integration of large language models (LLMs) with quantum-aware systems. These AI-powered models, capable of processing data and recognizing complex patterns, play a pivotal role in identifying vulnerabilities, predicting threats, and enhancing the resilience of security infrastructures. In quantum computing, LLMs offer new opportunities to stay ahead of cyber threats by simulating attack strategies and developing adaptive defense mechanisms. By harnessing the power of these tools, cybersecurity professionals can address current challenges while preparing for an era of quantum-enabled cyber threats. Leveraging Large Language Models for Quantum-Aware Cybersecurity explores the convergence of LLMs, cybersecurity, and quantum computing, providing an in-depth analysis of how these fields are being integrated to tackle emerging challenges in the digital security landscape. It covers foundational concepts, cutting-edge research, and practical applications, demonstrating how LLMs can be leveraged alongside quantum technologies to enhance threat detection, automate incident response, and build quantum-resilient security frameworks. This book covers topics such as artificial intelligence, computer engineering, natural language processing, and is a useful resource for computer engineers, security professionals, scientists, academicians, and researchers.
Formal Methods and Software Engineering
This book constitutes the proceedings of the 24th International Conference on Formal Methods and Software Engineering, ICFEM 2023, held in Brisbane, QLD, Australia, during November 21–24, 2023. The 13 full papers presented together with 8 doctoral symposium papers in this volume were carefully reviewed and selected from 34 submissions, the volume also contains one invited paper. The conference focuses on applying formal methods to practical applications and presents papers for research in all areas related to formal engineering methods.