Threat Hunting in the Cloud : Defending AWS, Azure and Other Cloud Platforms Against Cyberattacks

Threat Hunting in the Cloud : Defending AWS, Azure and Other Cloud Platforms Against Cyberattacks

المؤلف
سنة النشر
الناشر
اللغة
نوع الوثيقة
الموضوع الرئيسي
رمز الوثيقة

You'll learn : Key business and technical drivers of cybersecurity threat hunting frameworks in today's technological environment / Metrics available to assess threat hunting effectiveness regardless of an organization's size / How threat hunting works with vendor-specific single cloud security offerings and on multi-cloud implementations / A detailed analysis of key threat vectors such as email phishing, ransomware and nation state attacks / Comprehensive AWS and Azure "how to" solutions through the lens of MITRE Threat / Hunting Framework Tactics, Techniques and Procedures (TTPs) / Azure and AWS risk mitigation strategies to combat key TTPs such as privilege escalation, credential theft, lateral movement, defend against command & control systems, and prevent data exfiltration / Tools available on both the Azure and AWS cloud platforms which provide automated responses to attacks, and orchestrate preventative measures and recovery strategies / Many critical components for successful adoption of multi-cloud threat hunting framework such as Threat Hunting Maturity Model, Zero Trust Computing, Human Elements of Threat Hunting, Integration of Threat Hunting with Security Operation Centers (SOCs) and Cyber Fusion Centers / The Future of Threat Hunting with the advances in Artificial Intelligence, Machine Learning, Quantum Computing and the proliferation of IoT devices.



كتب مشابهة

img

Scalable data management for future hardware

Presents the results of the DFG priority program on scalable data management for future hardware. It details requirements and solutions of how modern and future hardware architectures can be leveraged to address the challenges in modern data management.the nine chapters of the book present a wide range of data management architectures in conjunction with current hardware developments, often related to applications in data analytics or machine learning. they cover topics such as hardware-accelerated query or event processing on FPGA, GPU, and multicore CPUs, scalable data management in data center networks or on modern memory and storage technologies, and operating system support.

img

New challenges in software engineering ; Vol 1

Explores the key challenges shaping the future of software development, including automation, AI-driven development, security-focused engineering, resilient and autonomous architectures, business process optimization, cloud computing, microservices, high-performance distributed systems, and sustainable technologies. Software engineering is undergoing a constant transformation, driven by rapid technological advances and evolving market demands. additionally, it delves into the ethical considerations of AI, the evolution of intuitive user interfaces, and the importance of multidisciplinary collaboration.

img

Fundamentals of manufacturing engineering using digital visualization

Offers a guide to core principles and practices of manufacturing engineering. It covers the design of, together with technological and measurement issues for, technical systems. Locating charts and setup schemes describing different machining processes are included. Concepts of product quality, with a focus on accuracy indicators, machining accuracy, roughness, and the impact of surface quality on exploitation properties are also explained. Furthermore, key machining methods, including turning, milling, hole machining, grinding, and gear machining, are analyzed in depth, covering their principles, applications, and techniques. The book is enriched by QR codes, linking to a mobile application presenting additional information about the content, for an interactive and extended learning experience. It also uses illustrations visualized with digital tools to promote a better understanding of the concepts.

img

AI in drug discovery

Constitutes the refereed proceedings of the First international workshop on ai in Drug Discovery, AIDD 2024, held as a part of the 33rd International Conference on Artificial Neural Networks, ICANN 2024, in Lugano, Switzerland, on September 19, 2024. These papers focus on various aspects of the rapidly evolving field of Artificial Intelligence (AI)-driven drug discovery in chemistry, including Big Data and advanced Machine Learning, eXplainable AI (XAI), Chemoinformatics, Use of deep learning to predict molecular properties, Modeling and prediction of chemical reaction data and Generative models.