86 citations · 102 across the 8 of their papers we have counts for
8 papers
BiLCNet : BiLSTM-Conformer Network for Encrypted Traffic Classification with 5G SA Physical Channel Records
Ke Ma, Jialiang Lu, Philippe Martins
Accurate and efficient traffic classification is vital for wireless network management, especially under encrypted payloads and dynamic application behavior, where traditional meth…
ToxiFrench: Benchmarking and Enhancing Language Models via CoT Fine-Tuning for French Toxicity Detection
Axel Delaval, Shujian Yang, Haicheng Wang +2
Detecting toxic content using language models is crucial yet challenging. While substantial progress has been made in English, toxicity detection in French remains underdeveloped,…
Exploring Multimodal Challenges in Toxic Chinese Detection: Taxonomy, Benchmark, and Findings
Shujian Yang, Shiyao Cui, Chuanrui Hu +5
Detecting toxic content using language models is important but challenging. While large language models (LLMs) have demonstrated strong performance in understanding Chinese, recent…
Real-time Traffic Classification for 5G NSA Encrypted Data Flows With Physical Channel Records
Xiao Fei, Philippe Martins, Jialiang Lu
The classification of fifth-generation New-Radio (5G-NR) mobile network traffic is an emerging topic in the field of telecommunications. It can be utilized for quality of service (…
An Interpretable Federated Learning-based Network Intrusion Detection Framework
Tian Dong, Song Li, Han Qiu +1
Learning-based Network Intrusion Detection Systems (NIDSs) are widely deployed for defending various cyberattacks. Existing learning-based NIDS mainly uses Neural Network (NN) as a…
Fingerprinting Multi-exit Deep Neural Network Models via Inference Time
Tian Dong, Han Qiu, Tianwei Zhang +3
Transforming large deep neural network (DNN) models into the multi-exit architectures can overcome the overthinking issue and distribute a large DNN model on resource-constrained s…
TDGIA:Effective Injection Attacks on Graph Neural Networks
Xu Zou, Qinkai Zheng, Yuxiao Dong +4
Graph Neural Networks (GNNs) have achieved promising performance in various real-world applications. However, recent studies have shown that GNNs are vulnerable to adversarial atta…
Hidden Backdoors in Human-Centric Language Models
Shaofeng Li, Hui Liu, Tian Dong +4
Natural language processing (NLP) systems have been proven to be vulnerable to backdoor attacks, whereby hidden features (backdoors) are trained into a language model and may only…