Muhammad Usman – författare

This book surveys state-of-the-art research on adversarial robustness of quantum machine learning algorithms. Despite their high efficiency and accuracy, classical ML and AI algorithms can be easily fooled by an adversary through manipulation or spoofing of data (also known as adversarial attacks), which poses serious security ramifications. On the other hand, the integration of quantum computing in ML and AI is progressing rapidly to create new quantum ML/AI models which are designed to fundamentally exploit quantum mechanical properties to gain advantages in aspects such as training speed or feature extraction accuracy. This raises the important question of whether quantum AI algorithms are as vulnerable as classical AI models. Recent work has shown that quantum AI algorithms are remarkably robust against adversarial attacks. This offers a unique opportunity to leverage quantum computing, specifically its unique properties like superposition and entanglement, to develop highly resistant quantum AI systems. This shift is crucial for enhancing the safety and reliability of AI in security-sensitive applications. This book provides a comprehensive overview of the research in the emerging field of quantum adversarial AI, presenting seminal work from world-leading quantum AI experts on quantum AI and its benchmarking against adversarial attacks. It provides an essential reference for graduate students and industry experts who are interested in quantum AI for security-sensitive autonomous systems.

This book presents the latest developments regarding a detailed mobile agent-enabled anomaly detection and verification system for resource constrained sensor networks; a number of algorithms on multi-aspect anomaly detection in sensor networks; several algorithms on mobile agent transmission optimization in resource constrained sensor networks; an algorithm on mobile agent-enabled in situ verification of anomalous sensor nodes; a detailed Petri Net-based formal modeling and analysis of the proposed system, and an algorithm on fuzzy logic-based cross-layer anomaly detection and mobile agent transmission optimization. As such, it offers a comprehensive text for interested readers from academia and industry alike.

This book presents the latest developments regarding a detailed mobile agent-enabled anomaly detection and verification system for resource constrained sensor networks;

The concept of open innovation (OI) has become a very popular topic during the last decade, with increasing number of SMEs embracing OI practices to gain competitive advantage. This edited volume is a timely opportunity to gather research on OI in SMEs, to investigate how OI is managed and implemented to determine the peculiarities compared to OI management in large companies, and to specify the consequences for future OI research.The book offers insights into the following topics: The state of the art on open innovation in SMEs; adopting open innovation in SMEs; interorganizational networks and innovation ecosystems; sectoral patterns of open innovation in SMEs; and measuring, evaluating and stimulating open innovation in SMEs.