Is AI a <i>Trick or T(h)reat</i> for Securing Programmable Data Planes?

In the Internet of Things era, the Internet demands extremely high-speed communication and data transformation. To this end, the tactile Internet has been proposed as a medium that provides the sense of touch ability, facilitating data transferability with extra-low latency in various applications ranging from industry, robotics, and healthcare to road traffic, education, and culture. Here, programmable networks are role players in approaching the tactile Internet’s low latency (≈ 1ms) pillar. Several functionalities – including security – are offloaded onto the network core employing programmable in-network pipelines. From the security perspective, Artificial Intelligence (AI) is another role player that enables the line-rate inference on the core network without involving the control plane. However, integrating AI-based security solutions in programmable devices is challenging mainly because of their constrained anatomy. Furthermore, such solutions inherit well-known adversarial AI vulnerabilities, representing an additional threat to programmable networks. Considering the above, this article discusses AI-based security solutions in programmable networks, focusing on the explored modalities of integrating AI models in programmable constrained network devices. Moreover, we elaborate on the challenges and risks of relying on AI for such mechanisms. Lastly, the article brings a visionary glimpse for future trends in this regard, raising some essential questions on the indispensability of AI for security functionalities and providing some alternative solutions.

Authors

Institutions

Publication Details

Journal
IEEE Network
Published
2024-08-29
DOI
https://doi.org/10.1109/mnet.2024.3451330
Citations
4
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
1.39
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Is AI a Trick or T(h)reat for Securing Programmable Data Planes?

Riccardo Lazzeretti, Mauro Conti, Enkeleda Bardhi
4 citations
IEEE Network
Adversarial Robustness in Machine Learning
1.39
article

Is AI a Trick or T(h)reat for Securing Programmable Data Planes?

Riccardo Lazzeretti, Mauro Conti, Enkeleda Bardhi
article en
4 citations

Abstract

In the Internet of Things era, the Internet demands extremely high-speed communication and data transformation. To this end, the tactile Internet has been proposed as a medium that provides the sense of touch ability, facilitating data transferability with extra-low latency in various applications ranging from industry, robotics, and healthcare to road traffic, education, and culture. Here, programmable networks are role players in approaching the tactile Internet’s low latency (≈ 1ms) pillar. Several functionalities – including security – are offloaded onto the network core employing programmable in-network pipelines. From the security perspective, Artificial Intelligence (AI) is another role player that enables the line-rate inference on the core network without involving the control plane. However, integrating AI-based security solutions in programmable devices is challenging mainly because of their constrained anatomy. Furthermore, such solutions inherit well-known adversarial AI vulnerabilities, representing an additional threat to programmable networks. Considering the above, this article discusses AI-based security solutions in programmable networks, focusing on the explored modalities of integrating AI models in programmable constrained network devices. Moreover, we elaborate on the challenges and risks of relying on AI for such mechanisms. Lastly, the article brings a visionary glimpse for future trends in this regard, raising some essential questions on the indispensability of AI for security functionalities and providing some alternative solutions.

IEEE NetworkVol. 38(6)
University of Padua (IT), Sapienza University of Rome (IT)
Openalex Percentile: Top 16%
Adversarial Robustness in Machine Learning
1.39
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Is AI a <i>Trick or T(h)reat</i> for Securing Programmable Data Planes? — Riccardo Lazzeretti, Mauro Conti, et al. · IEEE Network (2024) | TGRS Research Map | TGRS