Runqi Sui

Beijing University of Posts and Telecommunications

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i-10 index

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Co-Authors

Xuedong Wu

Beijing University of Posts and Telecommunications

Wenchuan Yang

Beijing University of Posts and Telecommunications

Wenchuan Yang

Beijing University of Posts and Telecommunications

Haoran Yu

Beijing University of Posts and Telecommunications

Baojiang Cui

Beijing University of Posts and Telecommunications

Baojiang Cui

Beijing University of Posts and Telecommunications

Tianyu Gu

Beijing University of Posts and Telecommunications

Haoran Dai

University of Shanghai for Science and Technology

Chaonan Wu

Beijing University of Posts and Telecommunications

Dun Liu

Beihang University

Cooperative Institutions

Beijing University of Posts and Telecommunications

National University of Defense Technology

Sun Yat-sen University

Dalian University of Technology

Sichuan University

Peking University

Tsinghua University

University of Electronic Science and Technology of China

Ningbo University

University of Shanghai for Science and Technology

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Feasibility Study of Edge Computing Empowered by Artificial Intelligence—A Quantitative Analysis Based on Large Models

Big Data and Cognitive Computing (2024)

Yan Chen Chaonan Wu Runqi Sui Jingjia Zhang

The advancement of artificial intelligence (AI) demands significant data and computational resources that have an adverse impact on the environment. To address this issue, a novel computing architecture that is both energy efficient and eco-friendly is urgently required. Edge computing has emerged as an increasingly popular solution to this problem. In this study, we explore the development history of edge computing and AI and analyze the potential of model quantization to link AI and edge computing. Our comparative analysis demonstrates that the quantization approach can effectively reduce the model’s size and accelerate model inference while maintaining its functionality, thereby enabling its deployment on edge devices. This research serves as a valuable guide and reference for future advancements in edge AI.

10.3390/bdcc8080094

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Enhanced anomaly traffic detection framework using BiGAN and contrastive learning

Cybersecurity (2024)

Haoran Yu Wenchuan Yang Baojiang Cui Runqi Sui Xuedong Wu

Abstract Abnormal traffic detection is a crucial topic in the field of network security. However, existing methods face many challenges when processing complex high-dimensional traffic data. Especially in dealing with redundant features, data sparsity and nonlinear features, traditional methods often suffer from high computational complexity and low detection efficiency. It is challenging to capture potential patterns in complex data effectively and cannot fully meet the needs of practical applications. To address these challenges, this paper proposes an enhanced anomaly traffic detection framework using bidirectional generative adversarial networks (BiGAN) and contrastive learning. This method preprocesses high-dimensional data through steps such as data cleaning, normalization, and clustering to improve data quality. It uses BiGAN and contrastive learning technology to enhance the model's feature representation capabilities. Experimental results show that the method proposed in this paper performs well on multiple traffic data sets and significantly improves the accuracy and efficiency of anomaly detection. Overall, the solution proposed in this paper effectively overcomes the limitations of existing methods in high-dimensional data processing and provides a more advanced abnormal traffic detection strategy.

Normalization

Feature Learning

Feature (linguistics)

10.1186/s42400-024-00297-7

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Deep neural network pruning method based on sensitive layers and reinforcement learning

Artificial Intelligence Review (2023)

Wenchuan Yang Haoran Yu Baojiang Cui Runqi Sui Tianyu Gu

Pruning

Feature (linguistics)

Residual neural network

Rank (graph theory)

10.1007/s10462-023-10566-5

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Citations (5)

The Security of RIP and IS-IS Routing Protocols: Research on Routing Attack Experiment

Xuedong Wu Wenchuan Yang Baojiang Cui Haoran Yu Runqi Sui

10.1109/icmiii62623.2024.00045

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Intriguing Properties of Universal Adversarial Triggers for Text Classification

Kexin Zhao Runqi Sui Xuedong Wu Wenchuan Yang

10.1109/icbase63199.2024.10762239

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Classification and Implementation of Relevance Data Based on Address Text

2021 China Automation Congress (CAC) (2021)

Wenchuan Yang Tianyu Gu Runqi Sui

Tasks based on address text are commonly seen in real life, such as location-based services based on geographic information systems, rapid localization and positioning systems for emergencies, and alignment of different address information systems. This article introduces the solutions to the classification problems of address text by comparing different models of different parameters, such as pre-training + nezha + rdropout and pre-training + nezha + swa + rdropout, in the classification of address information, obtaining different model effects. After using rdropout, it also shows a faster training speed and convergence speed.

Relevance

Training set

10.1109/cac53003.2021.9727884

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A Faster Method For Generating Chinese Text Summaries-Combining Extractive Summarization And Abstractive Summarization

Wenchuan Yang Tianyu Gu Runqi Sui

Extractive summarization and generative summarization are the two main ways to generate summarization.However,previous work treats both of them as two independent subtasks.In this paper,we obtain new summarization by combining extractive summarization and generative summarization.This method extracts the key information of the article firstly,and then generates the summarization of the extracted information.The experimental result shows that this method can significantly improve the quality of the generative text compared with extractive summarization,and can significantly improve the generative speed compared with generative summarization.

Multi-document summarization

Generative model

10.1145/3578741.3578752

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Renyi entropy-driven network traffic anomaly detection with dynamic threshold

Cybersecurity (2024)

Haoran Yu Wenchuan Yang Baojiang Cui Runqi Sui Xuedong Wu

Abstract Network traffic anomaly detection is a critical issue in network security. Existing Abnormal traffic detection methods rely on statistical-based or anomaly-based approaches, and these detection methods all require a full understanding of traffic characteristics and attack patterns. Information entropy has been widely studied in abnormal traffic detection because it can describe the distribution characteristics of network traffic. However, this method makes it difficult to cope with the timing and variability of network traffic. To address these challenges, this paper proposes a network traffic anomaly detection method based on Renyi entropy. Simultaneously, we introduce a fixed time window and utilize an improved EWMA model within this window to dynamically set thresholds for anomaly detection. Experimental results show that the method proposed in this paper is superior to popular abnormal traffic detection methods in terms of effectiveness and efficiency, it is better adapted to the dynamic changes of network traffic and provides a more reliable solution for anomaly detection.

Sliding window protocol

Anomaly (physics)

10.1186/s42400-024-00249-1

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EvilPromptFuzzer: generating inappropriate content based on text-to-image models

Cybersecurity (2024)

Juntao He Haoran Dai Runqi Sui Xuejing Yuan Dun Liu

Abstract Text-to-image (TTI) models provide huge innovation ability for many industries, while the content security triggered by them has also attracted wide attention. Considerable research has focused on content security threats of large language models (LLMs), yet comprehensive studies on the content security of TTI models are notably scarce. This paper introduces a systematic tool, named EvilPromptFuzzer, designed to fuzz evil prompts in TTI models. For 15 kinds of fine-grained risks, EvilPromptFuzzer employs the strong knowledge-mining ability of LLMs to construct seed banks, in which the seeds cover various types of characters, interrelations, actions, objects, expressions, body parts, locations, surroundings, etc. Subsequently, these seeds are fed into the LLMs to build scene-diverse prompts, which can weaken the semantic sensitivity related to the fine-grained risks. Hence, the prompts can bypass the content audit mechanism of the TTI model, and ultimately help to generate images with inappropriate content. For the risks of violence, horrible, disgusting, animal cruelty, religious bias, political symbol, and extremism, the efficiency of EvilPromptFuzzer for generating inappropriate images based on DALL.E 3 are greater than 30%, namely, more than 30 generated images are malicious among 100 prompts. Specifically, the efficiency of horrible, disgusting, political symbols, and extremism up to 58%, 64%, 71%, and 50%, respectively. Additionally, we analyzed the vulnerability of existing popular content audit platforms, including Amazon, Google, Azure, and Baidu. Even the most effective Google SafeSearch cloud platform identifies only 33.85% of malicious images across three distinct categories.

Content (measure theory)

10.1186/s42400-024-00279-9

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