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Found 22 papers, 2 papers with code
DT-DDNN: A Physical Layer Security Attack Detector in 5G RF Domain for CAVs
Unlike the existing jamming detection algorithms that mostly rely on network parameters, we introduce a double-threshold deep learning jamming detector by focusing on the SSB.
Deep Dict: Deep Learning-based Lossy Time Series Compressor for IoT Data
We propose Deep Dict, a deep learning-based lossy time series compressor designed to achieve a high compression ratio while maintaining decompression error within a predefined range.
Adversarial Machine Learning-Enabled Anonymization of OpenWiFi Data
Data privacy and protection through anonymization is a critical issue for network operators or data owners before it is forwarded for other possible use of data.
On the Impact of CDL and TDL Augmentation for RF Fingerprinting under Impaired Channels
This work uses a dataset that includes 5G, 4G, and WiFi samples, and it empowers a CDL+TDL-based augmentation technique in order to boost the learning performance of the DL model.
Rethinking Detection Based Table Structure Recognition for Visually Rich Document Images
However, existing detection-based models usually cannot perform as well as other types of solutions regarding cell-level TSR metrics, such as TEDS, and the underlying reasons limiting the performance of these models on the TSR task are also not well-explored.
Multidomain transformer-based deep learning for early detection of network intrusion
Timely response of Network Intrusion Detection Systems (NIDS) is constrained by the flow generation process which requires accumulation of network packets.
Host-Based Network Intrusion Detection via Feature Flattening and Two-stage Collaborative Classifier
Performance of XGBoost, which represents conventional ML, is evaluated.
Table Detection for Visually Rich Document Images
Table Detection (TD) is a fundamental task to enable visually rich document understanding, which requires the model to extract information without information loss.
Revisiting Table Detection Datasets for Visually Rich Documents
Moreover, to enrich the data sources, we propose a new ICT-TD dataset using the PDF files of Information and Communication Technologies (ICT) commodities, a different domain containing unique samples that hardly appear in open datasets.
Poisoning Attacks in Federated Edge Learning for Digital Twin 6G-enabled IoTs: An Anticipatory Study
However, we need to also consider the potential of attacks targeting the underlying AI systems (e. g., adversaries seek to corrupt data on the IoT devices during local updates or corrupt the model updates); hence, in this article, we propose an anticipatory study for poisoning attacks in federated edge learning for digital twin 6G-enabled IoT environments.
Efficient Information Sharing in ICT Supply Chain Social Network via Table Structure Recognition
Table Structure Recognition (TSR) aims to represent tables with complex structures in a machine-interpretable format so that the tabular data can be processed automatically.
Handling big tabular data of ICT supply chains: a multi-task, machine-interpretable approach
To transform the tabular data in electronic documents into a machine-interpretable format and provide layout and semantic information for information extraction and interpretation, we define a Table Structure Recognition (TSR) task and a Table Cell Type Classification (CTC) task.
Adversarial Machine Learning-Based Anticipation of Threats Against Vehicle-to-Microgrid Services
With an inference attack, an adversary can collect real-time data from the communication between smart microgrids and a 5G gNodeB to train a surrogate (i. e., shadow) model of the targeted classifier at the edge.
Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats
Advanced persistent threat (APT) is prominent for cybercriminals to compromise networks, and it is crucial to long-term and harmful characteristics.
Table Structure Recognition with Conditional Attention
Table Structure Recognition (TSR) problem aims to recognize the structure of a table and transform the unstructured tables into a structured and machine-readable format so that the tabular data can be further analysed by the down-stream tasks, such as semantic modeling and information retrieval.
Collaborative Self Organizing Map with DeepNNs for Fake Task Prevention in Mobile Crowdsensing
After pre-clustered legitimate tasks are separated from the original dataset, the remaining dataset is used to train a Deep Neural Network (DeepNN) to reach the ultimate performance goal.
Generative Adversarial Network-Driven Detection of Adversarial Tasks in Mobile Crowdsensing
To this end, we propose a two-level cascading classifier that combines the GAN discriminator with a binary classifier to prevent adversarial fake tasks.
On Cropped versus Uncropped Training Sets in Tabular Structure Detection
Prior works have addressed this problem under table detection and table structure detection tasks.
Risk-Aware Fine-Grained Access Control in Cyber-Physical Contexts
The risk levels are associated with access control decisions recommended by a security policy.
Federated Learning-Based Risk-Aware Decision toMitigate Fake Task Impacts on CrowdsensingPlatforms
Loss measurement considers the lost task values with respect to misclassification, where the final decision utilizes a risk-aware approach where the risk is formulated as a function of the utility loss.
Prior Knowledge Input to Improve LSTM Auto-encoder-based Characterization of Vehicular Sensing Data
Event characterization via vehicular sensors are utilized in safety and autonomous driving applications in vehicles.
A Comparative Study of AI-based Intrusion Detection Techniques in Critical Infrastructures
Volunteer computing uses Internet-connected devices (laptops, PCs, smart devices, etc.
