most citedRespond to Change with Constancy: Instruction-tuning with LLM for Non-I.I.D. Network Traffic Classification

15 citations · 15 across the 3 of their papers we have counts for

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cs.CR2025★ 15 cited

Respond to Change with Constancy: Instruction-tuning with LLM for Non-I.I.D. Network Traffic Classification

Xinjie Lin, Gang Xiong, Gaopeng Gou +4

Encrypted traffic classification is highly challenging in network security due to the need for extracting robust features from content-agnostic traffic data. Existing approaches fa…

cs.CR2025

POISONCRAFT: Practical Poisoning of Retrieval-Augmented Generation for Large Language Models

Yangguang Shao, Xinjie Lin, Haozheng Luo +4

Large language models (LLMs) have achieved remarkable success in various domains, primarily due to their strong capabilities in reasoning and generating human-like text. Despite th…