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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

Sina Samadi Gharehveran

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4 papers indexed here

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#reinforcement learning Open access Oct 2026

CTAM-RL: Continual Threat-Aware Memory for Continual Reinforcement Learning in Autonomous Cyber Defense

{ "upload_type": "software", "title": "CTAM-RL: Continual Threat-Aware Memory for Continual Reinforcement Learning in Autonomous Cyber Defense (v1.1.0)", "description": "Code, extracted attack profiles, and per-seed results for the manuscript 'A Continual Reinforcement Learning Framework for Autonomous Cyber Defense Un...

Kimia Memarpour, Bahar Memarpour, Kimia Shirini et al. · 0 citations
#reinforcement learning Open access Oct 2026

CTAM-RL: Continual Threat-Aware Memory for Continual Reinforcement Learning in Autonomous Cyber Defense

{ "upload_type": "software", "title": "CTAM-RL: Continual Threat-Aware Memory for Continual Reinforcement Learning in Autonomous Cyber Defense (v1.1.0)", "description": "Code, extracted attack profiles, and per-seed results for the manuscript 'A Continual Reinforcement Learning Framework for Autonomous Cyber Defense Un...

Kimia Memarpour, Bahar Memarpour, Kimia Shirini et al. · 0 citations
#graph neural networks Open access Oct 2026

Graph-Aware Reinforcement Learning for Adaptive APT Threat Hunting in Dynamic Enterprise Networks

Advanced Persistent Threats (APTs) are particularly challenging for enterprise intrusion detection because they are time-evolving, distributed, and difficult to detect under changing network conditions. Conventional machine-learning-based intrusion detection systems (IDSs) often rely on node-level features and may be v...

Bahar Memarpour, Kimia Memarpour, Kimia Shirini et al. · 0 citations
#reinforcement learning Open access Sep 2026

RAVA: a robust recurrent active vision agent for plant disease diagnosis under severe environmental noise

Abstract Deep learning models have achieved near-perfect accuracy in plant disease classification within controlled laboratory settings; however, their deployment in real-world agricultural environments is severely hindered by the “deployment gap”—a critical vulnerability to environmental corruptions such as sensor noi...

Nima Saeedi, Sana Gholinavaz, Sina Samadi Gharehveran et al. · 0 citations

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