Bertino Machine Learning Techniques for Cybersecurity 9783031282584

Варианты приобретения

Цена: 41920.00T
Кол-во:
о цене
Наличие: Отсутствует.
Возможна поставка под заказ. Дата поступления на склад уточняется после оформления заказа

Добавить в корзину

в Мои желания

Автор: Bertino
Название: Machine Learning Techniques for Cybersecurity
ISBN: 9783031282584
Издательство: Springer
Классификация:

Компьютерная безопасность

Безопасность работы в компьютерных сетях

Машинное обучение

ISBN-10: 3031282582
Обложка/Формат: Hardback
Страницы: 165
Вес: 0.49 кг.
Дата издания: 23.04.2023
Серия: Synthesis Lectures on Information Security, Privacy, and Trust
Язык: English
Издание: 1st ed. 2023
Иллюстрации: 30 illustrations, color; 7 illustrations, black and white; xii, 165 p. 37 illus., 30 illus. in color.
Размер: 240 x 168
Читательская аудитория: Professional & vocational
Основная тема: Computer Science
Ссылка на Издательство: Link
Рейтинг:
Поставляется из: Германии
Описание: This book explores machine learning (ML) defenses against the many cyberattacks that make our workplaces, schools, private residences, and critical infrastructures vulnerable as a consequence of the dramatic increase in botnets, data ransom, system and network denials of service, sabotage, and data theft attacks. The use of ML techniques for security tasks has been steadily increasing in research and also in practice over the last 10 years. Covering efforts to devise more effective defenses, the book explores security solutions that leverage machine learning (ML) techniques that have recently grown in feasibility thanks to significant advances in ML combined with big data collection and analysis capabilities. Since the use of ML entails understanding which techniques can be best used for specific tasks to ensure comprehensive security, the book provides an overview of the current state of the art of ML techniques for security and a detailed taxonomy of security tasks and corresponding ML techniques that can be used for each task. It also covers challenges for the use of ML for security tasks and outlines research directions. While many recent papers have proposed approaches for specific tasks, such as software security analysis and anomaly detection, these approaches differ in many aspects, such as with respect to the types of features in the model and the dataset used for training the models. In a way that no other available work does, this book provides readers with a comprehensive view of the complex area of ML for security, explains its challenges, and highlights areas for future research. This book is relevant to graduate students in computer science and engineering as well as information systems studies, and will also be useful to researchers and practitioners who work in the area of ML techniques for security tasks.
Дополнительное описание: Introduction.- Background on Machine Learning Techniques.- Security Policy earning.- Software Security Analysis.- Hardware Security Analysis.- Detection.- Attack Management.- Case Studies.- Main Challenges in the Use of ML for Security.- Concluding Remark

Machine Learning Techniques for Cybersecurity, Bertino