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Machine learning for cyber agentsattack and defence /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning for cyber agentsby Stanislav Abaimov, Maurizio Martellini.
其他題名:	attack and defence /
作者:	Abaimov, Stanislav.
其他作者:	Martellini, M.
出版者:	Cham :Springer International Publishing :2022.
面頁冊數:	xv, 227 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Machine learningSecurity measures.
電子資源:	https://doi.org/10.1007/978-3-030-91585-8
ISBN:	9783030915858$q(electronic bk.)

Machine learning for cyber agentsattack and defence /
Abaimov, Stanislav.

Machine learning for cyber agentsattack and defence /[electronic resource] :by Stanislav Abaimov, Maurizio Martellini. - Cham :Springer International Publishing :2022. - xv, 227 p. :ill., digital ;24 cm. - Advanced sciences and technologies for security applications,2363-9466. - Advanced sciences and technologies for security applications..

1. Introduction -- 2. Understanding Machine Learning -- 3. Defence -- 4. Attack -- 5. Feasibility and Misconceptions -- 6. International resonance -- 7. Prospects -- 8. Conclusion.

The cyber world has been both enhanced and endangered by AI. On the one hand, the performance of many existing security services has been improved, and new tools created. On the other, it entails new cyber threats both through evolved attacking capacities and through its own imperfections and vulnerabilities. Moreover, quantum computers are further pushing the boundaries of what is possible, by making machine learning cyber agents faster and smarter. With the abundance of often-confusing information and lack of trust in the diverse applications of AI-based technologies, it is essential to have a book that can explain, from a cyber security standpoint, why and at what stage the emerging, powerful technology of machine learning can and should be mistrusted, and how to benefit from it while avoiding potentially disastrous consequences. In addition, this book sheds light on another highly sensitive area - the application of machine learning for offensive purposes, an aspect that is widely misunderstood, under-represented in the academic literature and requires immediate expert attention.

ISBN: 9783030915858$q(electronic bk.)

Standard No.: 10.1007/978-3-030-91585-8doiSubjects--Topical Terms:

897594
Machine learning
--Security measures.

LC Class. No.: Q325.5 / .A33 2022

Dewey Class. No.: 006.31

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