A contrastive learning transformer embeds network flow sequences to enable correlation clustering that groups scanner sources consistently with labels.
2024 20th International Conference on Network and Service Management (CNSM) :1–9
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
netstacklat is a new low-overhead monitoring tool that records host network stack latency from early kernel processing to application delivery and was tested on 144 HTTP workload variants plus a Cloudflare deployment.
AGMARL-DKS uses per-node multi-agent RL with GNN state representations and stress-aware lexicographical ordering to outperform the default Kubernetes scheduler on fault tolerance, utilization, and cost for batch and mission-critical workloads.
SentinelSphere integrates an AI threat detector using an enhanced DNN on benchmark datasets with a fine-tuned quantized LLM for user training and awareness.
citing papers explorer
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Contrastive Learning and Correlation Clustering for Sequences of Network Telescope Data
A contrastive learning transformer embeds network flow sequences to enable correlation clustering that groups scanner sources consistently with labels.
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Waiting at the front door: Continuous monitoring of latency in the host network stack
netstacklat is a new low-overhead monitoring tool that records host network stack latency from early kernel processing to application delivery and was tested on 144 HTTP workload variants plus a Cloudflare deployment.
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AGMARL-DKS: An Adaptive Graph-Enhanced Multi-Agent Reinforcement Learning for Dynamic Kubernetes Scheduling
AGMARL-DKS uses per-node multi-agent RL with GNN state representations and stress-aware lexicographical ordering to outperform the default Kubernetes scheduler on fault tolerance, utilization, and cost for batch and mission-critical workloads.
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SentinelSphere: Integrating AI-Powered Real-Time Threat Detection with Cybersecurity Awareness Training
SentinelSphere integrates an AI threat detector using an enhanced DNN on benchmark datasets with a fine-tuned quantized LLM for user training and awareness.