Machine learning for network security management, attacks, and intrusions detection

• This chapter focuses on challenges, progress and pitfalls in applying ML to physical-layer security management. In the context of trustworthy networks, we motivate the need for automation in support of the work of network security professionals. We summarize the characteristics of known attack techniques targeting the physical layer and outline the framework for optical network security management. Supervised, semisupervised and unsupervised learning techniques that can aid automation of network security management are described with a focus on their performance requirements in the context of security. Accuracy, complexity, and interpretability of these techniques are examined on a use case of jamming and polarization scrambling attacks performed experimentally in a telecom operator network testbed. Finally, several open research challenges in the context of optical network security are outlined along with possible avenues to tackle some of them.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Telekommunikation (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Telecommunications (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Kommunikationssystem (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Communication Systems (hsv//eng)

NATURVETENSKAP  -- Data- och informationsvetenskap -- Systemvetenskap, informationssystem och informatik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Information Systems (hsv//eng)

Nyckelord

Semi-supervised learning

Physical-layer security

Infrastructure security

Intrusion detection

Unsupervised learning

Jamming

Supervised learning

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