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Found 52 papers, 18 papers with code
On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach
Despite many attempts in the existing literature to bridge this gap, most are limited to specific applications and focus mainly on feature extraction, often assuming extensive prior knowledge in signal processing.
Thwarting Cybersecurity Attacks with Explainable Concept Drift
The results show that FDE successfully identifies 85. 77 % of drifting features and showcases its utility in the DL adaptation method under the CD phenomenon.
Efficient Transformer-based Hyper-parameter Optimization for Resource-constrained IoT Environments
The hyper-parameter optimization (HPO) process is imperative for finding the best-performing Convolutional Neural Networks (CNNs).
Towards Intent-Based Network Management: Large Language Models for Intent Extraction in 5G Core Networks
The integration of Machine Learning and Artificial Intelligence (ML/AI) into fifth-generation (5G) networks has made evident the limitations of network intelligence with ever-increasing, strenuous requirements for current and next-generation devices.
Zero-Touch Networks: Towards Next-Generation Network Automation
The Zero-touch network and Service Management (ZSM) framework represents an emerging paradigm in the management of the fifth-generation (5G) and Beyond (5G+) networks, offering automated self-management and self-healing capabilities to address the escalating complexity and the growing data volume of modern networks.
On the Peak-to-Average Power Ratio of Vibration Signals: Analysis and Signal Companding for an Efficient Remote Vibration-Based Condition Monitoring
Vibration-based condition monitoring (VBCM) is widely utilized in various applications due to its non-destructive nature.
Leveraging Large Language Models for DRL-Based Anti-Jamming Strategies in Zero Touch Networks
As the dawn of sixth-generation (6G) networking approaches, it promises unprecedented advancements in communication and automation.
Forensic Data Analytics for Anomaly Detection in Evolving Networks
In the prevailing convergence of traditional infrastructure-based deployment (i. e., Telco and industry operational networks) towards evolving deployments enabled by 5G and virtualization, there is a keen interest in elaborating effective security controls to protect these deployments in-depth.
CorrFL: Correlation-Based Neural Network Architecture for Unavailability Concerns in a Heterogeneous IoT Environment
The generated CorrFL models for the unavailable IoT device from the available ones trained on the new environment are compared against models trained on different use cases, referred to as the benchmark model.
Modular Simulation Environment Towards OTN AI-based Solutions
The current trend for highly dynamic and virtualized networking infrastructure made automated networking a critical requirement.
Intelligent O-RAN Traffic Steering for URLLC Through Deep Reinforcement Learning
The goal of Next-Generation Networks is to improve upon the current networking paradigm, especially in providing higher data rates, near-real-time latencies, and near-perfect quality of service.
Similarity-Based Predictive Maintenance Framework for Rotating Machinery
For each feature type, three similarity metrics, namely structural similarity measure (SSM), cosine similarity, and Euclidean distance are used to measure the similarity between test signals and reference signals in the feature space.
A Multi-Stage Automated Online Network Data Stream Analytics Framework for IIoT Systems
In this paper, we propose a novel Multi-Stage Automated Network Analytics (MSANA) framework for concept drift adaptation in IIoT systems, consisting of dynamic data pre-processing, the proposed Drift-based Dynamic Feature Selection (DD-FS) method, dynamic model learning & selection, and the proposed Window-based Performance Weighted Probability Averaging Ensemble (W-PWPAE) model.
An NWDAF Approach to 5G Core Network Signaling Traffic: Analysis and Characterization
Data-driven approaches and paradigms have become promising solutions to efficient network performances through optimization.
IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective
Machine Learning (ML) approaches have shown their capacity for IoT data analytics.
A Hybrid Method for Condition Monitoring and Fault Diagnosis of Rolling Bearings With Low System Delay
The experimental results show that the proposed method can achieve high accuracy with low system delay.
A Model Drift Detection and Adaptation Framework for 5G Core Networks
Due to the dynamic nature of next-generation networks and their supported use cases and applications, model drift is a serious concern, which can deteriorate the performance of intelligent models deployed throughout the network.
LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of Vehicles
Modern vehicles, including autonomous vehicles and connected vehicles, have adopted an increasing variety of functionalities through connections and communications with other vehicles, smart devices, and infrastructures.
Towards Supporting Intelligence in 5G/6G Core Networks: NWDAF Implementation and Initial Analysis
Wireless networks, in the fifth-generation and beyond, must support diverse network applications which will support the numerous and demanding connections of today's and tomorrow's devices.
Intelligent Transportation Systems' Orchestration: Lessons Learned & Potential Opportunities
This growth has led to the need for new communication technologies that will promote this transformation.
Sound Event Classification in an Industrial Environment: Pipe Leakage Detection Use Case
An extensive experimental procedure was carried out on two datasets, one with background industrial noise and one without, to evaluate the validity of the proposed pipeline.
A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles
Modern vehicles, including autonomous vehicles and connected vehicles, are increasingly connected to the external world, which enables various functionalities and services.
An Attention-based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Series
As a substantial amount of multivariate time series data is being produced by the complex systems in Smart Manufacturing, improved anomaly detection frameworks are needed to reduce the operational risks and the monitoring burden placed on the system operators.
Concept Drift Detection in Federated Networked Systems
As next-generation networks materialize, increasing levels of intelligence are required.
PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams
As the number of Internet of Things (IoT) devices and systems have surged, IoT data analytics techniques have been developed to detect malicious cyber-attacks and secure IoT systems; however, concept drift issues often occur in IoT data analytics, as IoT data is often dynamic data streams that change over time, causing model degradation and attack detection failure.
Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection
Content delivery networks (CDNs) provide efficient content distribution over the Internet.
MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles
In this paper, the vulnerabilities of intra-vehicle and external networks are discussed, and a multi-tiered hybrid IDS that incorporates a signature-based IDS and an anomaly-based IDS is proposed to detect both known and unknown attacks on vehicular networks.
A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams
In recent years, with the increasing popularity of "Smart Technology", the number of Internet of Things (IoT) devices and systems have surged significantly.
Making a Case for Federated Learning in the Internet of Vehicles and Intelligent Transportation Systems
With the incoming introduction of 5G networks and the advancement in technologies, such as Network Function Virtualization and Software Defined Networking, new and emerging networking technologies and use cases are taking shape.
Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities
Machine learning (ML) provides a mechanism for humans to process large amounts of data, gain insights about the behavior of the data, and make more informed decision based on the resulting analysis.
Federated Intelligence for Active Queue Management in Inter-Domain Congestion
Active Queue Management (AQM) has been considered as a paradigm for the complicated network management task of mitigating congestion by controlling buffer of network link queues.
Relationship between Student Engagement and Performance in e-Learning Environment Using Association Rules
The field of e-learning has emerged as a topic of interest in academia due to the increased ease of accessing the Internet using using smart-phones and wireless devices.
DNS Typo-squatting Domain Detection: A Data Analytics & Machine Learning Based Approach
Moreover, the observed trends are validated by studying the same features in an unlabeled dataset using K-means clustering algorithm and through applying the developed ensemble learning classifier.
Detecting Botnet Attacks in IoT Environments: An Optimized Machine Learning Approach
Experimental results show that the proposed optimized framework has a high detection accuracy, precision, recall, and F-score, highlighting its effectiveness and robustness for the detection of botnet attacks in IoT environments.
Optimized Random Forest Model for Botnet Detection Based on DNS Queries
To that end, this paper proposes a novel optimized ML-based framework to detect botnets based on their corresponding DNS queries.
A Transfer Learning Framework for Anomaly Detection Using Model of Normality
We introduce a transfer learning framework for anomaly detection based on similarity measure with a Model of Normality (MoN) and show that with the proposed threshold settings, a significant performance improvement can be achieved.
Distance-Based Anomaly Detection for Industrial Surfaces Using Triplet Networks
In this paper, we address that challenge by training the CNN on surface texture patches with a distance-based anomaly detection objective instead.
Machine Learning Towards Enabling Spectrum-as-a-Service Dynamic Sharing
Adopting effective and efficient spectrum sharing mechanisms is in itself a challenging task given the multitude of levels and techniques that can be integrated to enable it.
Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection
Cyber-security garnered significant attention due to the increased dependency of individuals and organizations on the Internet and their concern about the security and privacy of their online activities.
Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection
Network attacks have been very prevalent as their rate is growing tremendously.
The Need for Advanced Intelligence in NFV Management and Orchestration
Through the adoption of Advanced Intelligence techniques such as Reinforcement Learning and Federated Learning, NSPs can leverage the benefits of traditional ML while simultaneously addressing the major challenges traditionally associated with it.
On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice
In this paper, optimizing the hyper-parameters of common machine learning models is studied.
A Machine Learning-Based Migration Strategy for Virtual Network Function Instances
With the growing demand for data connectivity, network service providers are faced with the task of reducing their capital and operational expenses while simultaneously improving network performance and addressing the increased demand.
Multi-split Optimized Bagging Ensemble Model Selection for Multi-class Educational Data Mining
Furthermore, this work aims to predict the students' performance at two stages of course delivery (20% and 50% respectively).
Ensemble-based Feature Selection and Classification Model for DNS Typo-squatting Detection
This paper builds on our previous work in [1], which only proposed majority-voting based classifier, by proposing an ensemble-based feature selection and bagging classification model to detect DNS typo-squatting attack.
Softwarization, Virtualization, & Machine Learning For Intelligent & Effective V2X Communications
Concepts such as softwarization, virtualization, and machine learning will be key components as innovative and flexible enablers of such networks.
Depth-Optimized Delay-Aware Tree (DO-DAT) for Virtual Network Function Placement
With the constant increase in demand for data connectivity, network service providers are faced with the task of reducing their capital and operational expenses while ensuring continual improvements to network performance.
Systematic Ensemble Model Selection Approach for Educational Data Mining
A plethora of research has been done in the past focusing on predicting student's performance in order to support their development.
Machine Learning for Performance-Aware Virtual Network Function Placement
The model takes several performance-related features from the network as an input and selects the placement of the various VNF instances on network servers with the objective of minimizing the delay between dependent VNF instances.
Tree-based Intelligent Intrusion Detection System in Internet of Vehicles
The results from the implementation of the proposed intrusion detection system on standard data sets indicate that the system has the ability to identify various cyber-attacks in the AV networks.
Intelligent Active Queue Management Using Explicit Congestion Notification
As more end devices are getting connected, the Internet will become more congested.
A Novel Algorithm for Real-time Procedural Generation of Building Floor Plans
Real-time generation of natural-looking floor plans is vital in games with dynamic environments.
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