Large Language Models In Cybersecurity
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Large Language Models in Cybersecurity
This open access book provides cybersecurity practitioners with the knowledge needed to understand the risks of the increased availability of powerful large language models (LLMs) and how they can be mitigated. It attempts to outrun the malicious attackers by anticipating what they could do. It also alerts LLM developers to understand their work's risks for cybersecurity and provides them with tools to mitigate those risks. The book starts in Part I with a general introduction to LLMs and their main application areas. Part II collects a description of the most salient threats LLMs represent in cybersecurity, be they as tools for cybercriminals or as novel attack surfaces if integrated into existing software. Part III focuses on attempting to forecast the exposure and the development of technologies and science underpinning LLMs, as well as macro levers available to regulators to further cybersecurity in the age of LLMs. Eventually, in Part IV, mitigation techniques that should allow safe and secure development and deployment of LLMs are presented. The book concludes with two final chapters in Part V, one speculating what a secure design and integration of LLMs from first principles would look like and the other presenting a summary of the duality of LLMs in cyber-security. This book represents the second in a series published by the Technology Monitoring (TM) team of the Cyber-Defence Campus. The first book entitled "Trends in Data Protection and Encryption Technologies" appeared in 2023. This book series provides technology and trend anticipation for government, industry, and academic decision-makers as well as technical experts.
Revolutionizing Cybersecurity With Deep Learning and Large Language Models
As cyber threats grow, national security measures struggle to keep pace with sophisticated attacks. Deep learning and large language models (LLMs) revolutionize cybersecurity by enabling advanced threat detection automated response mechanisms and analytics. AI technologies can analyze vast amounts of data, recognize patterns, and identify threats to security systems. Using deep learning and LLMs to transform cybersecurity is essential for addressing both their potential and the challenges that come with their adoption. Revolutionizing Cybersecurity With Deep Learning and Large Language Models explores the intersection of AI, cybersecurity, deep learning, and LLMs, and the potential of these technologies in safeguarding the digital world. It examines real-world applications, ethical challenges, and new technological advancements. This book covers topics such as artificial intelligence, cybersecurity, and threat detection, and is a useful resource for academicians, researchers, security professionals, computer engineers, and data scientists.
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.