Chu
370
● Self‐Defense
There exists a large body of literature now about ML algorithm for anomaly detection
and learning latent structures or patterns from network activities. Discovery of invariants
in functional, operational, causal and other relationships are crucial in many complex
cyber‐physical systems such as a smart grid [40]. The telco physical infrastructure itself
is such a system. ML can go beyond what traditional data analytics can deliver by discovering deep non‐linear, distributed, and time‐shifted relationships. Detection of these
kinds of complex relationships, and anomalies within them, is a huge step towards creating a large self‐defending NFV infrastructure and future applications that will run on it.
● Artificially‐Intelligent Customer Service
Beyond the wonders of Photo Captioning, Natural Language Processing (NLP) systems
and chat bots, AI and ML can also make the modeling of user needs much more intelligent. NFV allows these systems to have access to data far beyond just the Call Records
and mobile device tracking, for example, into end‐to‐end customer services. Examples
include seamless mobility between heterogeneous networks and voice or video emotion
detection of quality problem detections. NFV’s flexibility allows the system to address
the problem before a customer’s experience deteriorates. Security is a crucial part of
this customer experience. Static systems often put security vs. privacy, or security vs.
convenience into a zero‐sum game. ML can help us differentiate and make choices to
optimize the overall experience – without the hassle of making explicit trade‐offs every
time we face such a choice.
15.8 Conclusions
NFV and 5G security is a complex subject for a short chapter. We approached this topic
with an introduction to the essence of what NFV is all about, and showed that an NFV‐
based telco cloud is fundamental to what 5G can be – much more than a faster wireless
network. NFV brings to us new challenges in security, but also much more so in new
opportunities – not only because of the capabilities NFV enables, but also because
security is in the basic DNA of NFV from day one. These new capabilities will enable us
to be ready for 5G and the envisioned 5G use cases in industries as diverse as health
care, transportation, energy and media. Lastly, we introduced NFV‐based security
services, and looked into two areas where NFV can help transform how we meet security challenges in the future: policy‐based and machine‐learning‐based security services.
High‐level abstraction and data‐driven intelligent automation are absolutely essential to
solve the future security problems that will be several orders of higher‐scale magnitude.
References
1 NGMN Alliance (2015) 5G White Paper, February.
2 ETSI (2012) Network Functions Virtualization – An Introduction, Benefits, Enablers,
Challenges and Call for Action [Online]. Available at: https://portal.etsi.org/nfv/
nfv_white_paper.pdf
370
● Self‐Defense
There exists a large body of literature now about ML algorithm for anomaly detection
and learning latent structures or patterns from network activities. Discovery of invariants
in functional, operational, causal and other relationships are crucial in many complex
cyber‐physical systems such as a smart grid [40]. The telco physical infrastructure itself
is such a system. ML can go beyond what traditional data analytics can deliver by discovering deep non‐linear, distributed, and time‐shifted relationships. Detection of these
kinds of complex relationships, and anomalies within them, is a huge step towards creating a large self‐defending NFV infrastructure and future applications that will run on it.
● Artificially‐Intelligent Customer Service
Beyond the wonders of Photo Captioning, Natural Language Processing (NLP) systems
and chat bots, AI and ML can also make the modeling of user needs much more intelligent. NFV allows these systems to have access to data far beyond just the Call Records
and mobile device tracking, for example, into end‐to‐end customer services. Examples
include seamless mobility between heterogeneous networks and voice or video emotion
detection of quality problem detections. NFV’s flexibility allows the system to address
the problem before a customer’s experience deteriorates. Security is a crucial part of
this customer experience. Static systems often put security vs. privacy, or security vs.
convenience into a zero‐sum game. ML can help us differentiate and make choices to
optimize the overall experience – without the hassle of making explicit trade‐offs every
time we face such a choice.
15.8 Conclusions
NFV and 5G security is a complex subject for a short chapter. We approached this topic
with an introduction to the essence of what NFV is all about, and showed that an NFV‐
based telco cloud is fundamental to what 5G can be – much more than a faster wireless
network. NFV brings to us new challenges in security, but also much more so in new
opportunities – not only because of the capabilities NFV enables, but also because
security is in the basic DNA of NFV from day one. These new capabilities will enable us
to be ready for 5G and the envisioned 5G use cases in industries as diverse as health
care, transportation, energy and media. Lastly, we introduced NFV‐based security
services, and looked into two areas where NFV can help transform how we meet security challenges in the future: policy‐based and machine‐learning‐based security services.
High‐level abstraction and data‐driven intelligent automation are absolutely essential to
solve the future security problems that will be several orders of higher‐scale magnitude.
References
1 NGMN Alliance (2015) 5G White Paper, February.
2 ETSI (2012) Network Functions Virtualization – An Introduction, Benefits, Enablers,
Challenges and Call for Action [Online]. Available at: https://portal.etsi.org/nfv/
nfv_white_paper.pdf