Machine Learning for Network Attacks Classification and Statistical Evaluation of Adversarial Learning Methodologies for Synthetic Data Generation
Fuente:
arXiv
Saved in:
| Main Authors: | Zarkadis, Iakovos-Christos, Douligeris, Christos |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
XAI and Statistical Analysis for Reliable Intrusion Detection in the UAVIDS-2025 Dataset: From Tree to Hybrid and Tabular DNN Ensembles
by: Zarkadis, Iakovos-Christos, et al.
Published: (2026)
by: Zarkadis, Iakovos-Christos, et al.
Published: (2026)
Bridging Data Barriers among Participants: Assessing the Potential of Geoenergy through Federated Learning
by: Peng, Weike, et al.
Published: (2024)
by: Peng, Weike, et al.
Published: (2024)
Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots
by: Xin, Xi, et al.
Published: (2024)
by: Xin, Xi, et al.
Published: (2024)
DynaMark: A Reinforcement Learning Framework for Dynamic Watermarking in Industrial Machine Tool Controllers
by: Aftabi, Navid, et al.
Published: (2025)
by: Aftabi, Navid, et al.
Published: (2025)
Diffusion-Driven Synthetic Tabular Data Generation for Enhanced DoS/DDoS Attack Classification
by: B, Aravind, et al.
Published: (2026)
by: B, Aravind, et al.
Published: (2026)
Adversarial Machine Learning Threats to Spacecraft
by: Thummala, Rajiv, et al.
Published: (2024)
by: Thummala, Rajiv, et al.
Published: (2024)
On Evaluating The Performance of Watermarked Machine-Generated Texts Under Adversarial Attacks
by: Liu, Zesen, et al.
Published: (2024)
by: Liu, Zesen, et al.
Published: (2024)
VFLGAN: Vertical Federated Learning-based Generative Adversarial Network for Vertically Partitioned Data Publication
by: Yuan, Xun, et al.
Published: (2024)
by: Yuan, Xun, et al.
Published: (2024)
Vision Transformer with Adversarial Indicator Token against Adversarial Attacks in Radio Signal Classifications
by: Zhang, Lu, et al.
Published: (2025)
by: Zhang, Lu, et al.
Published: (2025)
Adversarial Attacks on Machine Learning-Aided Visualizations
by: Fujiwara, Takanori, et al.
Published: (2024)
by: Fujiwara, Takanori, et al.
Published: (2024)
On the Robustness of Bayesian Neural Networks to Adversarial Attacks
by: Bortolussi, Luca, et al.
Published: (2022)
by: Bortolussi, Luca, et al.
Published: (2022)
SAFELOC: Overcoming Data Poisoning Attacks in Heterogeneous Federated Machine Learning for Indoor Localization
by: Singampalli, Akhil, et al.
Published: (2024)
by: Singampalli, Akhil, et al.
Published: (2024)
Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients
by: Yuan, Jinsheng, et al.
Published: (2025)
by: Yuan, Jinsheng, et al.
Published: (2025)
Input-Specific and Universal Adversarial Attack Generation for Spiking Neural Networks in the Spiking Domain
by: Raptis, Spyridon, et al.
Published: (2025)
by: Raptis, Spyridon, et al.
Published: (2025)
Investigating Imperceptibility of Adversarial Attacks on Tabular Data: An Empirical Analysis
by: He, Zhipeng, et al.
Published: (2024)
by: He, Zhipeng, et al.
Published: (2024)
Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks
by: Eghtesad, Taha, et al.
Published: (2023)
by: Eghtesad, Taha, et al.
Published: (2023)
Enhancing Security in Deep Reinforcement Learning: A Comprehensive Survey on Adversarial Attacks and Defenses
by: Yichao, Wu, et al.
Published: (2025)
by: Yichao, Wu, et al.
Published: (2025)
Data Overvaluation Attack and Truthful Data Valuation in Federated Learning
by: Zheng, Shuyuan, et al.
Published: (2025)
by: Zheng, Shuyuan, et al.
Published: (2025)
Conditional Adversarial Fragility in Financial Machine Learning under Macroeconomic Stress
by: Baviskar, Samruddhi
Published: (2025)
by: Baviskar, Samruddhi
Published: (2025)
Machine-learned Adversarial Attacks against Fault Prediction Systems in Smart Electrical Grids
by: Ardito, Carmelo, et al.
Published: (2023)
by: Ardito, Carmelo, et al.
Published: (2023)
Watermarking Generative Tabular Data
by: He, Hengzhi, et al.
Published: (2024)
by: He, Hengzhi, et al.
Published: (2024)
Practical Adversarial Attacks on Stochastic Bandits via Fake Data Injection
by: Zeng, Qirun, et al.
Published: (2025)
by: Zeng, Qirun, et al.
Published: (2025)
False Data Injection Attack Detection in Edge-based Smart Metering Networks with Federated Learning
by: Uddin, Md Raihan, et al.
Published: (2024)
by: Uddin, Md Raihan, et al.
Published: (2024)
Federated Learning Resilient to Byzantine Attacks and Data Heterogeneity
by: Zuo, Shiyuan, et al.
Published: (2024)
by: Zuo, Shiyuan, et al.
Published: (2024)
UIFV: Data Reconstruction Attack in Vertical Federated Learning
by: Yang, Jirui, et al.
Published: (2024)
by: Yang, Jirui, et al.
Published: (2024)
Adversarial Robustness in Financial Machine Learning: Defenses, Economic Impact, and Governance Evidence
by: Baviskar, Samruddhi
Published: (2025)
by: Baviskar, Samruddhi
Published: (2025)
A Comprehensive Study of Supervised Machine Learning Models for Zero-Day Attack Detection: Analyzing Performance on Imbalanced Data
by: Lotfi, Zahra, et al.
Published: (2025)
by: Lotfi, Zahra, et al.
Published: (2025)
The Data Minimization Principle in Machine Learning
by: Ganesh, Prakhar, et al.
Published: (2024)
by: Ganesh, Prakhar, et al.
Published: (2024)
Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017
by: Xu, Zhaoyang, et al.
Published: (2025)
by: Xu, Zhaoyang, et al.
Published: (2025)
Privacy-Preserving Federated Learning via Homomorphic Adversarial Networks
by: Dong, Wenhan, et al.
Published: (2024)
by: Dong, Wenhan, et al.
Published: (2024)
DATABench: Evaluating Dataset Auditing in Deep Learning from an Adversarial Perspective
by: Shao, Shuo, et al.
Published: (2025)
by: Shao, Shuo, et al.
Published: (2025)
GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation Models
by: Luo, Haozheng, et al.
Published: (2025)
by: Luo, Haozheng, et al.
Published: (2025)
Backdoor Attack on Vertical Federated Graph Neural Network Learning
by: Yang, Jirui, et al.
Published: (2024)
by: Yang, Jirui, et al.
Published: (2024)
Optimal Transport-Guided Adversarial Attacks on Graph Neural Network-Based Bot Detection
by: Mukherjee, Kunal, et al.
Published: (2026)
by: Mukherjee, Kunal, et al.
Published: (2026)
A Survey on Adversarial Robustness of LiDAR-based Machine Learning Perception in Autonomous Vehicles
by: Kim, Junae, et al.
Published: (2024)
by: Kim, Junae, et al.
Published: (2024)
A General Black-box Adversarial Attack on Graph-based Fake News Detectors
by: Zhu, Peican, et al.
Published: (2024)
by: Zhu, Peican, et al.
Published: (2024)
Medusa: Cross-Modal Transferable Adversarial Attacks on Multimodal Medical Retrieval-Augmented Generation
by: Shang, Yingjia, et al.
Published: (2025)
by: Shang, Yingjia, et al.
Published: (2025)
Untargeted Adversarial Attack on Knowledge Graph Embeddings
by: Zhao, Tianzhe, et al.
Published: (2024)
by: Zhao, Tianzhe, et al.
Published: (2024)
Adversarial Attacks on Transformers-Based Malware Detectors
by: Jakhotiya, Yash, et al.
Published: (2022)
by: Jakhotiya, Yash, et al.
Published: (2022)
Relationship between Uncertainty in DNNs and Adversarial Attacks
by: Ogonna, Mabel, et al.
Published: (2024)
by: Ogonna, Mabel, et al.
Published: (2024)
Similar Items
-
XAI and Statistical Analysis for Reliable Intrusion Detection in the UAVIDS-2025 Dataset: From Tree to Hybrid and Tabular DNN Ensembles
by: Zarkadis, Iakovos-Christos, et al.
Published: (2026) -
Bridging Data Barriers among Participants: Assessing the Potential of Geoenergy through Federated Learning
by: Peng, Weike, et al.
Published: (2024) -
Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots
by: Xin, Xi, et al.
Published: (2024) -
DynaMark: A Reinforcement Learning Framework for Dynamic Watermarking in Industrial Machine Tool Controllers
by: Aftabi, Navid, et al.
Published: (2025) -
Diffusion-Driven Synthetic Tabular Data Generation for Enhanced DoS/DDoS Attack Classification
by: B, Aravind, et al.
Published: (2026)