HALIDS: A hardware-assisted machine learning IDS for in-network monitoring

B Brandino, E Grampin, K Dietz… - 2024 8th Network …, 2024 - ieeexplore.ieee.org
Early decision-making at the network device level is crucial for network security. This entails
moving beyond traditional forwarding functions towards more intelligent network devices …

MDQ: A QoS-Congestion Aware Deep Reinforcement Learning Approach for Multi-Path Routing in SDN

LPA Sanchez, Y Shen, M Guo - Journal of Network and Computer …, 2025 - Elsevier
The challenge of link overutilization in networking persists, prompting the development of
load-balancing methods such as multi-path strategies and flow rerouting. However …

In-Network DDoS Mitigation Mechanism for Vehicle Road Cooperation Network With Victim-Centric Approach

Y Liu, S Shao, S Guo, Z Zang… - IEEE Internet of Things …, 2025 - ieeexplore.ieee.org
Vehicle Road Cooperation (VRC) services closely related to personal safety impose
stringent requirements on reliability and real-time performance. However, the growing trend …

Detecting Attacks at Switching Speed: AI/ML and Active Learning for in-Network Monitoring in Data Planes

B Brandino, P Casas, E Grampín - 2024 IEEE 32nd …, 2024 - ieeexplore.ieee.org
Early decision-making at the network device is crucial for network security. This entails
moving beyond traditional forwarding functions towards more intelligent network devices …

DUNE: Distributed Inference in the User Plane

B Bütün, D de Andres Hernandez… - IEEE International …, 2025 - dspace.networks.imdea.org
The deployment of Machine Learning (ML) models in the user plane enables line-rate in-
network inference, significantly reducing latency and improving the scalability of functions …

Optimal Flow Admission Control in Edge Computing via Safe Reinforcement Learning

A Fox, F De Pellegrini, F Faticanti, E Altman… - arxiv preprint arxiv …, 2024 - arxiv.org
With the uptake of intelligent data-driven applications, edge computing infrastructures
necessitate a new generation of admission control algorithms to maximize system …

Agree to Disagree: Exploring Consensus of XAI Methods for ML-based NIDS

K Dietz, M Hajizadeh, J Schleicher… - … on Network and …, 2024 - ieeexplore.ieee.org
The increasing complexity and frequency of cyber attacks require Network Intrusion
Detection Systems (NIDS) that can adapt to evolving threats. Artificial intelligence (AI) …

CyberSentinel: Efficient Anomaly Detection in Programmable Switch using Knowledge Distillation

S Mittal - arxiv preprint arxiv:2412.16693, 2024 - arxiv.org
The increasing volume of traffic (especially from IoT devices) is posing a challenge to the
current anomaly detection systems. Existing systems are forced to take the support of the …

Leveraging In-band Network Telemetry for Automated DDoS Detection in Production Programmable Networks: The AmLight Use Case

H Sahin, J Bezerra, I Brito, R Frez… - SC24-W: Workshops …, 2024 - ieeexplore.ieee.org
Programmable data planes have provided great flexibility in defining the behaviors of packet
forwarding switches, routers, and network interface cards (NICs). The In-band Network …

Real-time Financial Anomaly Detection in SAP ERP Systems Using Ensemble Learning Surya Sai Ram Parimi

SS Parimi - Available at SSRN 4934842, 2024 - papers.ssrn.com
Financial anomaly detection is paramount in SAP ERP systems to safeguard against fraud,
errors, and operational inefficiencies. This survey paper explores the application of …