arXiv Machine Learning
Temporal Analysis of NetFlow Datasets for Network Intrusion Detection Systems
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Aug 26
Autonomous Cyber Defense: Real-Time Attack Detection and Mitigation in Software-Defined Networks Using Machine Learning
arXiv:2608.22075v2 Announce Type: replace-cross Abstract: Adversaries now move faster than manual response processes can absorb. The average eCrime breakout time, that is, the interval between initia...
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).
arXiv Machine Learning
2d ago
Multi-Class, Multi-Tier Network Intrusion Detection: A Comprehensive and Reproducible Benchmark
arXiv:2609.36039v1 Announce Type: cross Abstract: Machine learning (ML) and deep learning (DL) have dominated Intrusion Detection System (IDS) research in recent years. Unfortunately, many existing s...
arXiv Machine Learning
Jun 5
An Improved CNN-LSTM Based Intrusion Detection System for IoT Networks
arXiv:2606. 05776v1 Announce Type: cross Abstract: With the rapid proliferation of IoT devices, security concerns have dramatically escalated and intrusion detection systems have become critical for protecting networked environments.
arXiv AI
Aug 17
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.
arXiv Machine Learning
Jun 10
nCMD: Benign-Anchored Feature Selection for Imbalanced Network Intrusion Detection
arXiv:2606. 09934v1 Announce Type: new Abstract: Feature selection is critical for network intrusion detection systems (NIDS) operating under high-dimensional, highly imbalanced traffic, as found in operational and defense networks.