Bøker av Alexander Kott

This book offers a structured overview and a comprehensive guide to the emerging field of Autonomous Intelligent Cyber Defense Agents (AICA). The book discusses the current technical issues in autonomous cyber defense and offers information on practical design approaches. The material is presented in a way that is accessible to non-specialists, with tutorial information provided in the initial chapters and as needed throughout the book. The reader is provided with clear and comprehensive background and reference material for each aspect of AICA.Today¿s cyber defense tools are mostly watchers. They are not active doers. They do little to plan and execute responses to attacks, and they don¿t plan and execute recovery activities. Response and recovery ¿ core elements of cyber resilience ¿ are left to human cyber analysts, incident responders and system administrators. This is about to change. The authors advocate this vision, provide detailed guide to how such a visioncan be realized in practice, and its current state of the art.This book also covers key topics relevant to the field, including functional requirements and alternative architectures of AICA, how it perceives and understands threats and the overall situation, how it plans and executes response and recovery, how it survives threats, and how human operators deploy and control AICA. Additionally, this book covers issues of testing, risk, and policy pertinent to AICA, and provides a roadmap towards future R&D in this field.This book targets researchers and advanced students in the field of cyber defense and resilience. Professionals working in this field as well as developers of practical products for cyber autonomy will also want to purchase this book.

Explores the expanding realm of net-centric warfare and the challenges methods of war pose for military commanders, from the platoon leader to senior leadership at the highest levels. This book represents the culmination of a research programme conducted by the US Military and the Defense Advanced Research Projects Agency.

This dissertation proposes a computational technique for automated "invention" of conceptual schemes of thermal systems. The input provided to the automated problem solver is a description of the streams entering and leaving the system. The output is a network of elementary processes: compression, expansion, heating, cooling, and chemical processes. The problem solver seeks a network that is feasible, and offers an optimal (or at least favorable) combination of energy and capital costs. The synthesis process is modeled as a heuristic search conducted in a state-space of all possible design versions. The main ideas of the dissertation have been implemented in a computer program called TED, which invented a number of nontrivial schemes.TED starts with an initial state (or states), which may be either proposed by the user or generated automatically. TED evaluates each state using a special technique of exergy analysis applied to an infinitesimal temperature interval. This allows us to describe the thermal system by several integral characteristics which are functions of temperature. One particularly important integral characteristic - a measure of system's Second Law infeasibility - is introduced in this work; it allows a uniform treatment of both feasible and infeasible design states.TED then selects the most promising of the available designs. This selection is guided by a specialized search algorithm BP* which is shown to be probabilistically admissible. The results of the exergy analysis are used to perform a look-ahead evaluation of the design states. BP* also uses backpropagation of the state evaluation function to reduce the amount of backtracking.TED then improves the selected design by applying one of the transforming operators and thereby generating a new design. Each transformation involves addition of an incremental network of thermal processes to the original state and reduces either irreversibility (exergy loss) or infeasibility of the thermal system. The application of the transformations is controlled by a heuristic move generation function that selects the most promising transformations. The new design is added to the database of the available design states.The search continues with these evaluate-select-transform iterations until an (approximately) optimal design is found.