Cover Image

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Xu, Zhaoyang, Liu, Yunbo
Format:	Preprint
Published:	2025
Subjects:	Cryptography and Security Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Semi-Supervised Learning for Anomaly Traffic Detection via Bidirectional Normalizing Flows
by: Dang, Zhangxuan, et al.
Published: (2024)

Research on Dynamic Data Flow Anomaly Detection based on Machine Learning
by: Wang, Liyang, et al.
Published: (2024)

TrafficLLM: Enhancing Large Language Models for Network Traffic Analysis with Generic Traffic Representation
by: Cui, Tianyu, et al.
Published: (2025)

Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning
by: Shuvo, Md Nahid Hasan, et al.
Published: (2025)

Integrating Graph Neural Networks with Scattering Transform for Anomaly Detection
by: Zoubir, Abdeljalil, et al.
Published: (2024)

MULTI-LF: A Continuous Learning Framework for Real-Time Malicious Traffic Detection in Multi-Environment Networks
by: Rustam, Furqan, et al.
Published: (2025)

A Customer Level Fraudulent Activity Detection Benchmark for Enhancing Machine Learning Model Research and Evaluation
by: Jing, Phoebe, et al.
Published: (2024)

Hierarchical Local-Global Feature Learning for Few-shot Malicious Traffic Detection
by: Peng, Songtao, et al.
Published: (2025)

Bridging Privacy and Robustness for Trustworthy Machine Learning
by: Zhang, Xiaojin, et al.
Published: (2024)

TAS-GNN: A Status-Aware Signed Graph Neural Network for Anomaly Detection in Bitcoin Trust Systems
by: Xue, Chang, et al.
Published: (2026)

Learning-Based Link Anomaly Detection in Continuous-Time Dynamic Graphs
by: Poštuvan, Tim, et al.
Published: (2024)

TempoNet: Learning Realistic Communication and Timing Patterns for Network Traffic Simulation
by: Moore, Kristen, et al.
Published: (2026)

Machine Learning Transferability for Malware Detection
by: Vieira, César, et al.
Published: (2026)

Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection
by: Gabrielli, Edoardo, et al.
Published: (2023)

Retrieval Augmented Anomaly Detection (RAAD): Nimble Model Adjustment Without Retraining
by: Pastoriza, Sam, et al.
Published: (2025)

Content-Aware Attack Detection in LLM Agent Tool-Call Traffic: An Empirical Study of Features, Architectures, and Evaluation Protocols
by: Zavrak, Sultan
Published: (2026)

RMSL: Weakly-Supervised Insider Threat Detection with Robust Multi-sphere Learning
by: Wang, Yang, et al.
Published: (2025)

Black-box Adversarial Attacks on Network-wide Multi-step Traffic State Prediction Models
by: Poudel, Bibek, et al.
Published: (2021)

Scalable Hierarchical AI-Blockchain Framework for Real-Time Anomaly Detection in Large-Scale Autonomous Vehicle Networks
by: Shit, Rathin Chandra, et al.
Published: (2025)

KnowGraph: Knowledge-Enabled Anomaly Detection via Logical Reasoning on Graph Data
by: Zhou, Andy, et al.
Published: (2024)

Engineering Attack Vectors and Detecting Anomalies in Additive Manufacturing
by: Hasan, Md Mahbub, et al.
Published: (2026)

VISAT: Benchmarking Adversarial and Distribution Shift Robustness in Traffic Sign Recognition with Visual Attributes
by: Yu, Simon, et al.
Published: (2025)

Adversarial Robustness in Financial Machine Learning: Defenses, Economic Impact, and Governance Evidence
by: Baviskar, Samruddhi
Published: (2025)

A Cryptographic Perspective on Mitigation vs. Detection in Machine Learning
by: Gluch, Greg, et al.
Published: (2025)

Impacts of Data Preprocessing and Hyperparameter Optimization on the Performance of Machine Learning Models Applied to Intrusion Detection Systems
by: Lima, Mateus Guimarães, et al.
Published: (2024)

Adversarial Augmentation and Active Sampling for Robust Cyber Anomaly Detection
by: Benabderrahmane, Sidahmed, et al.
Published: (2025)

Research on Key Technologies for Cross-Cloud Federated Training of Large Language Models
by: Yang, Haowei, et al.
Published: (2024)

Locking Machine Learning Models into Hardware
by: Clifford, Eleanor, et al.
Published: (2024)

LogGuardQ: A Cognitive-Enhanced Reinforcement Learning Framework for Cybersecurity Anomaly Detection in Security Logs
by: de Sousa, Umberto Gonçalves
Published: (2025)

A Graph Transformer-Driven Approach for Network Robustness Learning
by: Zhang, Yu, et al.
Published: (2023)

Machine Learning for Network Attacks Classification and Statistical Evaluation of Adversarial Learning Methodologies for Synthetic Data Generation
by: Zarkadis, Iakovos-Christos, 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)

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)

Command & Control (C2) Traffic Detection Via Algorithm Generated Domain (Dga) Classification Using Deep Learning And Natural Language Processing
by: Felix, Maria Milena Araujo
Published: (2025)

A Study on the Importance of Features in Detecting Advanced Persistent Threats Using Machine Learning
by: Hallaji, Ehsan, et al.
Published: (2025)

NoPhish: Efficient Chrome Extension for Phishing Detection Using Machine Learning Techniques
by: Thaqi, Leand, et al.
Published: (2024)

Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detection
by: Zhang, Han, et al.
Published: (2024)

ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning
by: Xu, Zhangchen, et al.
Published: (2024)

Enhancing Cloud Network Resilience via a Robust LLM-Empowered Multi-Agent Reinforcement Learning Framework
by: Peng, Yixiao, et al.
Published: (2026)

CyberCScope: Mining Skewed Tensor Streams and Online Anomaly Detection in Cybersecurity Systems
by: Nakamura, Kota, et al.
Published: (2025)