Название: Redefining Security With Cyber AI
Автор: Marwan Omar, Hewa Majeed Zangana
Издательство: IGI Global
Год: 2024
Страниц: 287
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB

In the rapidly evolving digital landscape, the importance of cybersecurity has never been more critical. With the increasing sophistication of cyber threats, traditional security measures often fall short in providing adequate protection. Cyber Artificial Intelligence (AI) offers advanced capabilities to detect, prevent, and respond to attacks in real time. As cyber threats continue to grow in complexity and frequency, the integration of AI into cybersecurity frameworks is not just advantageous but essential for maintaining robust and resilient defenses. Redefining Security With Cyber AI delves into the profound transformation of security paradigms brought about by the advent of AI. This book explores the intricate dance between the ever-expanding frontiers of digital technology and the AI-driven mechanisms that aim to safeguard them. Covering topics such as artificial neural networks, intrusion detection, and large language models, this book is an excellent resource for cybersecurity professionals, AI and Machine Learning researchers, IT executives and managers, policy makers and regulators, postgraduate students and educators, academicians, and more.

Chapter 1 delves into the realm of Transformer models, hailed for their prowess in natural language processing. However, concerns over their computational efficiency and environmental impact loom large. The chapter explores the application of weight pruning—a strategic reduction of model parameters—as an optimization strategy for Transformer architectures. Through rigorous experimentation, various pruning methodologies are scrutinized, shedding light on their impact on model performance, size, andcomputational demands. The findings underscore the potential of weight pruning to mitigate the challenges posed by Transformer models, paving the way for more efficient and sustainable AI solutions.

Firewalls stand as stalwarts in the realm of network security, yet their efficacy hinges on effective management. Chapter 2 embarks on a journey through firewall rule analyzers, probing their functionalities, methodologies, and implications. By integrating advanced technologies like machine learning and SDN, the chapter explores avenues for automation and compliance enhancement. Offering insights into firewall policy reconnaissance, anomaly detection, and rule optimization, it charts a course toward fortifying network security posture through proactive management and informed decision-making.

Efficiency lies at the heart of effective large language models (LLMs), and Chapter 3 sets out to uncover the elusive balance between model size, performance, and computational resources. Through innovative methodologies that enable parameter sharing, the chapter seeks to streamline LLM development without compromising on learning capabilities. By offering valuable insights and tools for creating more efficient and sustainable LLMs, it charts a course toward a future where AI language modeling is both accessible and environmentally conscious.

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In Chapter 12, the Linear Substitution Cipher, long revered for its encryption capabilities, undergoes a transformative journey propelled by advancements in algorithmic refinement and AI systems. The authors explore the efficacy of advanced cipher variants in overcoming vulnerabilities and bolstering cryptographic robustness. By navigating the intricate interplay between conventional techniques and AI-driven enhancements, it charts a course toward more resilient cryptosystems.






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