In an era where traditional firewalls and antivirus software no longer stand a chance against sophisticated cyberattacks, this book provides the essential roadmap you need to secure next-generation cloud, IoT, and decentralized systems against catastrophic data breaches and ransomware. Over the past few years, traditional cybersecurity measures have proven insufficient against the ever-growing sophistication of cyberattacks. As technology advances, particularly with the proliferation of cloud computing, the Internet of Things, and decentralized applications, cybersecurity professionals are faced with new and more challenging security risks, ranging from data breaches to cyber-espionage and ransomware attacks. This book delves into the transformative impact of artificial intelligence and deep learning on contemporary cybersecurity practices. It provides a detailed exploration of how cutting-edge AI and deep learning methodologies are being leveraged to secure digital ecosystems, mitigate emerging threats, and strengthen cryptographic protocols. By integrating traditional security approaches with advanced machine learning and neural networks, the book offers readers a robust understanding of the next-generation tools and frameworks that are shaping the cybersecurity landscape. In addition to theoretical foundations, the book features practical case studies and real-world applications. Readers will gain insights into the use of AI for securing cloud environments, IoT networks, and mobile platforms. Emerging challenges such as securing AI models against adversarial attacks and ethical considerations in AI-driven cybersecurity are also discussed. With contributions from experts in academia and industry, the book offers a balanced perspective, making it a valuable resource for researchers, practitioners, and students at advanced undergraduate, graduate, and doctoral levels.
This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.
P. Abrahamsson, O. Salo, Jussi Ronkainen et al.· arXiv.org· 727 citations· ⚡54
The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.
M. Pikkarainen, Jukka Haikara, O. Salo et al.· Empirical Software Engineeri...· 401 citations· ⚡48
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al.· Information and Software Tec...· 394 citations· ⚡54
The perception of the impact of agile methods is predominantly positive, and several challenge areas were discovered, but based on this study, agile methods are here to stay.
M. Laanti, O. Salo, P. Abrahamsson· Information and Software Tec...· 260 citations· ⚡20
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.