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Found 22 papers, 3 papers with code
Data Augmentation for Traffic Classification
Data Augmentation (DA) -- enriching training data by adding synthetic samples -- is a technique widely adopted in Computer Vision (CV) and Natural Language Processing (NLP) tasks to improve models performance.
Toward Generative Data Augmentation for Traffic Classification
Data Augmentation (DA)-augmenting training data with synthetic samples-is wildly adopted in Computer Vision (CV) to improve models performance.
Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input Representation
Over the last years we witnessed a renewed interest toward Traffic Classification (TC) captivated by the rise of Deep Learning (DL).
Many or Few Samples? Comparing Transfer, Contrastive and Meta-Learning in Encrypted Traffic Classification
The popularity of Deep Learning (DL), coupled with network traffic visibility reduction due to the increased adoption of HTTPS, QUIC and DNS-SEC, re-ignited interest towards Traffic Classification (TC).
User-aware WLAN Transmit Power Control in the Wild
Yet each network comes with its unique distribution of users in space, calling for a power control that adapts to users' probabilities of presence, for example, placing the areas with higher interference probabilities where user density is the lowest.
"It's a Match!" -- A Benchmark of Task Affinity Scores for Joint Learning
Some tasks may benefit from being learned together while others may be detrimental to one another.
Cross-network transferable neural models for WLAN interference estimation
Airtime interference is a key performance indicator for WLANs, measuring, for a given time period, the percentage of time during which a node is forced to wait for other transmissions before to transmitting or receiving.
Rare Yet Popular: Evidence and Implications from Labeled Datasets for Network Anomaly Detection
Anomaly detection research works generally propose algorithms or end-to-end systems that are designed to automatically discover outliers in a dataset or a stream.
Local Evaluation of Time Series Anomaly Detection Algorithms
In recent years, specific evaluation metrics for time series anomaly detection algorithms have been developed to handle the limitations of the classical precision and recall.
How Much is Enough? A Study on Diffusion Times in Score-based Generative Models
Score-based diffusion models are a class of generative models whose dynamics is described by stochastic differential equations that map noise into data.
Landing AI on Networks: An equipment vendor viewpoint on Autonomous Driving Networks
The tremendous achievements of Artificial Intelligence (AI) in computer vision, natural language processing, games and robotics, has extended the reach of the AI hype to other fields: in telecommunication networks, the long term vision is to let AI fully manage, and autonomously drive, all aspects of network operation.
Quality Monitoring and Assessment of Deployed Deep Learning Models for Network AIOps
Artificial Intelligence (AI) has recently attracted a lot of attention, transitioning from research labs to a wide range of successful deployments in many fields, which is particularly true for Deep Learning (DL) techniques.
A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classification
Traffic classification, i. e. the identification of the type of applications flowing in a network, is a strategic task for numerous activities (e. g., intrusion detection, routing).
Neural combinatorial optimization beyond the TSP: Existing architectures under-represent graph structure
Recent years have witnessed the promise that reinforcement learning, coupled with Graph Neural Network (GNN) architectures, could learn to solve hard combinatorial optimization problems: given raw input data and an evaluator to guide the process, the idea is to automatically learn a policy able to return feasible and high-quality outputs.
Accelerating Deep Learning Classification with Error-controlled Approximate-key Caching
While Deep Learning (DL) technologies are a promising tool to solve networking problems that map to classification tasks, their computational complexity is still too high with respect to real-time traffic measurements requirements.
Human readable network troubleshooting based on anomaly detection and feature scoring
Network troubleshooting is still a heavily human-intensive process.
HURRA! Human readable router anomaly detection
This paper presents HURRA, a system that aims to reduce the time spent by human operators in the process of network troubleshooting.
Thinkback: Task-SpecificOut-of-Distribution Detection
The increased success of Deep Learning (DL) has recently sparked large-scale deployment of DL models in many diverse industry segments.
A First Look at Class Incremental Learning in Deep Learning Mobile Traffic Classification
The recent popularity growth of Deep Learning (DL) re-ignited the interest towards traffic classification, with several studies demonstrating the accuracy of DL-based classifiers to identify Internet applications' traffic.
FENXI: Deep-learning Traffic Analytics at the Edge
The design of FENXI decouples forwarding operations and traffic analytics which operates at different granularities i. e., packet and flow levels.
Deep Learning and Traffic Classification: Lessons learned from a commercial-grade dataset with hundreds of encrypted and zero-day applications
The increasing success of Machine Learning (ML) and Deep Learning (DL) has recently re-sparked interest towards traffic classification.
Heterogeneous Data-Aware Federated Learning
Federated learning (FL) is an appealing concept to perform distributed training of Neural Networks (NN) while keeping data private.
