Digital twin network: Opportunities and challenges
The proliferation of emergent network applications (eg, AR/VR, telesurgery, real-time
communications) is increasing the difficulty of managing modern communication networks …
communications) is increasing the difficulty of managing modern communication networks …
Advanced deep learning models for 6G: overview, opportunities and challenges
The advent of the sixth generation of mobile communications (6G) ushers in an era of
heightened demand for advanced network intelligence to tackle the challenges of an …
heightened demand for advanced network intelligence to tackle the challenges of an …
XAI meets mobile traffic classification: Understanding and improving multimodal deep learning architectures
The increasing diffusion of mobile devices has dramatically changed the network traffic
landscape, with Traffic Classification (TC) surging into a fundamental role while facing new …
landscape, with Traffic Classification (TC) surging into a fundamental role while facing new …
Netllm: Adapting large language models for networking
Many networking tasks now employ deep learning (DL) to solve complex prediction and
optimization problems. However, current design philosophy of DL-based algorithms entails …
optimization problems. However, current design philosophy of DL-based algorithms entails …
Network planning with deep reinforcement learning
Network planning is critical to the performance, reliability and cost of web services. This
problem is typically formulated as an Integer Linear Programming (ILP) problem. Today's …
problem is typically formulated as an Integer Linear Programming (ILP) problem. Today's …
AI/ML for network security: The emperor has no clothes
Several recent research efforts have proposed Machine Learning (ML)-based solutions that
can detect complex patterns in network traffic for a wide range of network security problems …
can detect complex patterns in network traffic for a wide range of network security problems …
Deepaid: Interpreting and improving deep learning-based anomaly detection in security applications
Unsupervised Deep Learning (DL) techniques have been widely used in various security-
related anomaly detection applications, owing to the great promise of being able to detect …
related anomaly detection applications, owing to the great promise of being able to detect …
Mousika: Enable general in-network intelligence in programmable switches by knowledge distillation
Given the power efficiency and Tbps throughput of packet processing, several works are
proposed to offload the decision tree (DT) to programmable switches, ie, in-network …
proposed to offload the decision tree (DT) to programmable switches, ie, in-network …
Learning tailored adaptive bitrate algorithms to heterogeneous network conditions: A domain-specific priors and meta-reinforcement learning approach
Internet adaptive video streaming is a typical form of video delivery that leverages adaptive
bitrate (ABR) algorithms to provide video services with high quality of experience (QoE) for …
bitrate (ABR) algorithms to provide video services with high quality of experience (QoE) for …
Unveiling the potential of graph neural networks for robust intrusion detection
The last few years have seen an increasing wave of attacks with serious economic and
privacy damages, which evinces the need for accurate Network Intrusion Detection Systems …
privacy damages, which evinces the need for accurate Network Intrusion Detection Systems …