arXiv AI August 17, 2026 By Igor Cherepanov, David Sessler, Alex Ulmer, Felix Wagner, Throsten May, J\"orn Kohlhammer

Interactive Analysis of Global Explanations using Aggregated Class Activation Maps for Network Data

arXiv:2608. 13575v1 Announce Type: cross Abstract: Recent machine learning (ML) advances have demonstrated that deep learning (DL) achieves impressive results in different application domains, including the classification of computer network traffic to corresponding applications.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 9

XAInomaly: Explainable and Interpretable Deep Contractive Autoencoder for O-RAN Traffic Anomaly Detection

arXiv:2502. 09194v1 Announce Type: cross Abstract: Generative Artificial Intelligence (AI) techniques have become integral part in advancing next generation wireless communication systems by enabling sophisticated data modeling and feature extraction for enhanced network performance.

By Osman Tugay Basaran, Falko Dressler

safety

arXiv AI

Jun 17

Timestamp-Aware Spatio-Temporal Graph Contrastive Learning for Network Intrusion Detection

arXiv:2606. 17109v1 Announce Type: cross Abstract: Given their effectiveness in modeling the relational structure among network traffic flows, graph neural networks (GNNs) have been widely adopted in network intrusion detection systems (NIDSs).

By Jianli Dai, Guangwei Wu, Jiacheng Li, Weiping Wang, An He, Xinjun Xiao

benchmarks

arXiv AI

Jun 30

PLAA: Packet-level Adversarial Attacks in Network Traffic Detection

arXiv:2606. 28439v1 Announce Type: cross Abstract: Deep neural networks (DNNs) are widely applied in Network-based Intrusion Detection System (NIDS) due to their high accuracy.

By Jinhao You, Zan Zhou, Shujie Yang, Yi Sun, Lei Zhang, Changqiao Xu

computer-vision safety

arXiv AI

Jul 2

Cross-Domain Generalization Failure in Lightweight Intrusion Detection Models for IIoT Networks

arXiv:2607. 00553v1 Announce Type: cross Abstract: Lightweight machine learning models are increasingly proposed for intrusion detection in Industrial Internet of Things (IIoT) networks due to their suitability for resource-constrained edge deployment.

By MD Azizul Hakim, Md Shihab Uddin, Talha Ibne Anis

efficiency safety

arXiv Machine Learning

Aug 7

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining

arXiv:2608. 05605v1 Announce Type: cross Abstract: Research and Education Networks (RENs) serve as critical infrastructure for scientific discovery, yet they face a unique security paradox: their normal traffic patterns which are characterized by massive, bursty "elephant flows" are statistically indistinguishable from volumetric attacks such as DDoS to conventional monitoring systems.

By Mohammad Arafath Uddin Shariff, Byrav Ramamurthy

agents benchmarks

arXiv Machine Learning

Jul 13

XAI and Statistical Analysis for Reliable Intrusion Detection in the UAVIDS-2025 Dataset: From Tree to Hybrid and Tabular DNN Ensembles

arXiv:2605. 13922v2 Announce Type: replace-cross Abstract: During thDuring the last few years, the term Mechanistic Interpretability, a specific area, under the umbrella of explainable artificial intelligence (XAI), has been introduced, to explain the decisions made by complex machine learning (ML) models in critical systems like UAV intrusion detection systems (UAVIDS).

By Iakovos-Christos Zarkadis, Christos Douligeris

safety

Related stories

XAInomaly: Explainable and Interpretable Deep Contractive Autoencoder for O-RAN Traffic Anomaly Detection

Timestamp-Aware Spatio-Temporal Graph Contrastive Learning for Network Intrusion Detection

PLAA: Packet-level Adversarial Attacks in Network Traffic Detection

Cross-Domain Generalization Failure in Lightweight Intrusion Detection Models for IIoT Networks

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining

XAI and Statistical Analysis for Reliable Intrusion Detection in the UAVIDS-2025 Dataset: From Tree to Hybrid and Tabular DNN Ensembles