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IEEE Symposium on computers and Communications (ISCC)

作者： Liang Jiao Jianqiang Li Wenxiu Zhang Tianyu Cui Yi Zhou Yujia Zhu Qingyun Liu Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences National Computer Network Emergency Response Technical Team/Coordination Center of China Shandong Branch National Computer Network Emergency Response Technical Team/Coordination Center School of Information Science and Engineering University of Jinan Zhongguancun Laboratory

ISBN: (数字)9798350354232

ISBN: (纸本)9798350354249

The Encrypted Domain Name System (DNS) and Encrypted Server Name Indication (ESNI) are recently proposed to enhance network security and privacy protection; we refer to these schemes collectively as domain name encryption technologies. Previous research has shown that the destination IP address accessed by the user cannot be associated with common web services such as websites because a large number of websites are hosted through cloud or CDN over IPv6. However, encrypted DNS, as an internet infrastructure service, is typically deployed independently by the service provider rather than hosted through cloud or CDN. In this paper, we propose a method to discover the unique service provider of encrypted DNS resolvers on a large-scale encrypted traffic with TLS1.3 support over IPv6. The model utilizes a Siamese network to determine whether two IPv6 resolver addresses belong to the same service provider of encrypted DNS, even if the DNS query is protected by ESNI. Through a comprehensive analysis of two real-world datasets, which include encrypted DNS data and common web data, we find that the implementation of TLS1.3, especially ESNI, does not impact the association of encrypted DNS server addresses. Our model achieves an accuracy rate of 95.29%.

关键词： Measurement Cloud computing Privacy Correlation Web services network security Domain Name System Cryptography Servers Protection

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Msqat: A Multi-Dimension Non-Intrusive Speech Quality Assessment Transformer Utilizing Self-Supervised Representations

SSRN

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SSRN 2023年

作者： Shen, Kailai Yan, Diqun Dong, Li Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China

Convolutional neural networks (CNNs) have been widely utilized as the main building block for many \textit{non-intrusive} speech quality assessment (NISQA) methods. A new trend is to add a self-attention mechanism based on CNN to better capture long-term global content. However, it is not clear whether the pure attention-based network is sufficient to obtain good performance in NISQA. To this end, a framework named \textbf{M}ulti-dimension \textit{non-intrusive} \textbf{S}peech \textbf{Q}uality \textbf{A}ssessment \textbf{T}ransformer (MSQAT) is proposed. To strengthen the interactions of various speech regions between local and global, three multi-dimensional modules are devised. Additionally, speech quality varies not only in different frames, but also in different frequencies. Thus, a two-branch structure is designed to better evaluate the quality of speech. Experimental results demonstrate that the proposed MSQAT has state-of-the-art performance on three standard datasets (NISQA Corpus, Tencent Corpus, and PSTN Corpus). © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Fast Crop Pest Detection Using Lightweight Feature Extraction and Knowledge Distillation

Fast Crop Pest Detection Using Lightweight Feature Extractio...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ze Yang Xianliang Jiang Guang Jin Junkai Huang Jie Bai Dingxin Yu Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China

Pest detection is critical for achieving effective pest control. However, the current deep learning-based pest detection algorithm is unsuitable for deployment on resource-limited edge devices due to its extensive computation and long inference time. Although lightweight models have been widely used for practical detection, their insufficient feature extraction capability leads to a decline in detection accuracy. This paper proposes a fast algorithm for crop pest detection based on lightweight feature extraction and knowledge distillation. Firstly, we introduce partial convolution and propose a lightweight feature extraction module, C3Faster, which reduces the model's computation and speeds up model inference while ensuring effective feature extraction. Secondly, we use knowledge distillation to improve the model's detection accuracy by using teacher networks to assist in training. Finally, we created a dataset, CropPest6, consisting of six crop pest categories and conducted experiments. The experimental results demonstrate that our method reduces the detection time, number of parameters, and computation by 17%, 38%, and 44%, respectively, compared to the baseline model. Furthermore, our method achieves 93.9% Precision, 93.6% Recall, and 97.5% mean Average Precision (mAP), demonstrating its practical suitability for fast crop pest detection.

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A Trusted Privacy-Preserving Model for Cross-Chain Transactions Based on zk_SNARKs

A Trusted Privacy-Preserving Model for Cross-Chain Transacti...

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International Conference on networking and network Applications (NaNA)

作者： Yichuan Wang Jianhuan Tie Xinhong Hei Li Zhao Xiaohui Zhang School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technolog Xi'an China

Currently, the privacy protection technology of blockchain is not mature enough, such as user data leakage and lack of anonymity exist, and these problems are especially serious when conducting cross-chain transactions. In this paper, we propose a method of cross-chain transactions based on zk_SNARKs in a trusted environment for cross-chain transactions for privacy protection. Cross-chain transactions are performed through the features of zk_SNARKs such as efficiency, privacy, and verifiability. zk_SNARKs needs to generate trusted settings with the help of a third party during the transaction, and the process has serious privacy issues, and the private parameters generated during the transaction may be maliciously attacked and thus obtained by attackers. To address this problem, we use a hardware technology SGX for trusted computing to encrypt and store some private parameters generated by zk_SNARKs, thus ensuring the security of cross-chain transactions. The experimental results show that the privacy protection scheme using SGX for cross-chain transactions takes less time than the traditional transaction privacy protection, with a verification time of 1.2s for a number of 10 sidechains, and is more efficient than the traditional cross-chain transaction privacy protection.

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Non-Intrusive Speech Quality Assessment: A Survey

SSRN

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SSRN 2023年

作者： Shen, Kailai Yan, Diqun Hu, Jing Ye, Zhe Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China

Speech quality is a critical consideration for applications such as speech enhancement, coding, transmission, and synthesis. Accurately evaluating the quality of degraded speech without a reference is particularly challenging. As a result, non-intrusive speech quality assessment has been extensively researched due to its efficiency and ease of deployment. This survey comprehensively reviews these studies. Commonly used speech quality assessment databases are reviewed, as they serve as validation sets for objective metrics. Non-intrusive speech quality assessment methods are categorized and reviewed, and the performance of state-of-the-art and classical speech quality methods is compared. The survey presents an overview of classic algorithms and recent advancements in the field of non-intrusive speech quality assessment. © 2023, The Authors. All rights reserved.

关键词： Speech enhancement

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Fully Spiking Actor network with Intra-layer Connections for Reinforcement Learning

arXiv

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arXiv 2024年

作者： Chen, Ding Peng, Peixi Huang, Tiejun Tian, Yonghong Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Technology Peking University Beijing100871 China Network Intelligence Research PengCheng Laboratory Shenzhen518066 China School of Electronics Computer Engineering Peking University Shenzhen518055 China

With the help of special neuromorphic hardware, spiking neural networks (SNNs) are expected to realize artificial intelligence (AI) with less energy consumption. It provides a promising energy-efficient way for realistic control tasks by combining SNNs with deep reinforcement learning (DRL). In this paper, we focus on the task where the agent needs to learn multi-dimensional deterministic policies to control, which is very common in real scenarios. Recently, the surrogate gradient method has been utilized for training multi-layer SNNs, which allows SNNs to achieve comparable performance with the corresponding deep networks in this task. Most existing spike-based RL methods take the firing rate as the output of SNNs, and convert it to represent continuous action space (i.e., the deterministic policy) through a fully-connected (FC) layer. However, the decimal characteristic of the firing rate brings the floating-point matrix operations to the FC layer, making the whole SNN unable to deploy on the neuromorphic hardware directly. To develop a fully spiking actor network without any floating-point matrix operations, we draw inspiration from the non-spiking interneurons found in insects and employ the membrane voltage of the non-spiking neurons to represent the action. Before the non-spiking neurons, multiple population neurons are introduced to decode different dimensions of actions. Since each population is used to decode a dimension of action, we argue that the neurons in each population should be connected in time domain and space domain. Hence, the intra-layer connections are used in output populations to enhance the representation capacity. This mechanism exists extensively in animals and has been demonstrated effectively. Finally, we propose a fully spiking actor network with intra-layer connections (ILC-SAN). Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art performance on continuous control tasks from OpenAI gym.

关键词： Reinforcement learning

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Performance of fiber-based QAM/FSO systems in turbulence with anisotropic tilt angle and random angular jitter

arXiv

引用

arXiv 2025年

作者： Zhai, Chao Xue, Zhenyuan School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao066004 China Hebei Key Laboratory of Marine Perception Network and Data Processing Northeastern University at Qinhuangdao Qinhuangdao066004 China

Nowadays, the subsistent anisotropic non-Kolmogorov (ANK) turbulence models are all established on the supposition that the long axis of turbulence cell ought to be level with the ground. Nevertheless, Beason et al. and Wang et al. have illustrated through their recent experimental results that there is an anisotropic tilt angle in the turbulence cell, i.e., the long axis of turbulence cell is probably not level with the ground but has a particular angle with the ground. For the practical free-space optical (FSO) communication link, bias error and random angular jitter are critical elements which influence the fiber-based FSO communication system performance. In this paper, employing the new ANK turbulence spectrum models in the horizontal link with anisotropic tilt angle, we derive the normalized probability density function (PDF) for Gamma-Gamma distribution with anisotropic tilt angle, and the PDF of fiber-coupling efficiency in the presence of bias error, anisotropic tilt angle, and random angular jitter. And then the average bit error rate (BER) expression of fiber-based J × Q rectangular quadrature amplitude modulation (QAM) FSO systems for a plane wave transmission through the weak ANK horizontal link in the presence of bias error, anisotropic tilt angle, and random angular jitter is developed. © 2025, CC BY-NC-ND.

关键词： Quadrature amplitude modulation

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Petri Net-Based Attack Modeling for Industrial Control System networks 3rd

Petri Net-Based Attack Modeling for Industrial Control Syste...

引用

3rd China Intelligent Robotics Annual Conference, CCF CIRAC 2022

作者： Wang, Yichuan Lv, Zhaoqi Zhang, Yaling Xiao, Yeqiu Hei, Xinhong School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

ISBN: (纸本)9789819903009

Industrial control systems have been deeply involved in industrial facilities and infrastructure. With the integrated development of industry and information technology, industrial control systems are tied to the Internet and may be threatened by cyber attacks. Modeling analysis and security evaluation are significant to guarantee industrial control system security. For in-depth research of security in the industrial control network, we first provide a cyber attack strategy for programmable logic controllers with respect to critical nodes in industrial control systems. Then, we explore and validate the viability of attacks based on the proposed strategy through simulations and visualization. Finally, Petri net modeling is utilized to depict both cyber attacks and defensive techniques. The results in this paper demonstrate that the proposed attack strategy gives new ideas for us to investigate industrial control cyber assaults, while the modeling and analytic methodologies in this paper are also useful for system weaknesses analysis. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： network security

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Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment

arXiv

引用

arXiv 2024年

作者： Zhang, Congzhi Zhang, Linhai Wu, Jialong He, Yulan Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King’s College London United Kingdom The Alan Turing Institute United Kingdom

Despite the notable advancements of existing prompting methods, such as In-Context Learning and Chain-of-Thought for Large Language Models (LLMs), they still face challenges related to various biases. Traditional debiasing methods primarily focus on the model training stage, including approaches based on data augmentation and reweighting, yet they struggle with the complex biases inherent in LLMs. To address such limitations, the causal relationship behind the prompting methods is uncovered using a structural causal model, and a novel causal prompting method based on front-door adjustment is proposed to effectively mitigate LLMs biases. In specific, causal intervention is achieved by designing the prompts without accessing the parameters and logits of LLMs. The chain-of-thought generated by LLM is employed as the mediator variable and the causal effect between input prompts and output answers is calculated through front-door adjustment to mitigate model biases. Moreover, to accurately represent the chain-of-thoughts and estimate the causal effects, contrastive learning is used to fine-tune the encoder of chain-of-thought by aligning its space with that of the LLM. Experimental results show that the proposed causal prompting approach achieves excellent performance across seven natural language processing datasets on both open-source and closed-source LLMs. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Deep Learning-Driven Simultaneous Layout Decomposition and Mask Optimization

Deep Learning-Driven Simultaneous Layout Decomposition and M...

引用

作者： Zhong, Wei Hu, Shuxiang Ma, Yuzhe Yang, Haoyu Ma, Xiuyuan Yu, Bei DUTRU International School of Information Science and Engineering Dalian University of Technology Dalian China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian116024 China

Combining multiple patterning lithography (MPL) and optical proximity correction (OPC) pushes the limit of 193-nm wavelength lithography to go further. Considering that layout decomposition may generate plenty of solutions with diverse printabilities, relying on conventional mask optimization (MO) process to select the best candidate for manufacturing is computationally expensive. Therefore, an accurate and efficient printability estimation is crucial and can significantly accelerate the layout decomposition and MO (LDMO) flow. In this article, we propose a convolutional neural network (CNN)-based prediction and integrate it into our new high-performance LDMO framework. The optimization process can be considerably improved as the decomposition quality has been inferred in the early phase. To facilitate the network training and ensure better estimation accuracy, we develop sampling strategies for both layout and decomposition. Moreover, we enhance the layout sampling approach by adopting autoencoder to distance evaluation that promises superior sampling results. The experimental results demonstrate the effectiveness and the efficiency of the proposed algorithms. © 1982-2012 IEEE.

关键词： Manufacture

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