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2021 4th International Conference on computer Information science and Application technology, CISAT 2021

作者： Sun, Shimin Zhang, Xinchao Huang, Wentian Xu, Aixin Wang, Xiaofan Han, Li Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Computer Science and Technology Tiangong University Tianjin300387 China National Engineering Laboratory for Computer Virus Prevention and Control Technology School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China

Most of the detection methods against DDoS attacks are based on periodic detection, which leads to high communication overhead, untimely detection, and slow attack response. This paper proposes a passive abnormality detection approach. First, we record the two flow-table characteristics of the regular switches. Then, based on dynamic threshold method and Grubbs outlier test method, we make a determination of abnormal switches. This method reduces the amount of data duplication and regular traffic collection. Moreover, we use Support Vector Machine (SVM) algorithm to evaluate the performance of the passive anomaly detection method. The experiment results show a better performance than active period DDoS attack detection approaches. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.

关键词： Support vector machines

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Improving Code Search with Multi-Modal Momentum Contrastive Learning

Improving Code Search with Multi-Modal Momentum Contrastive ...

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IEEE/ACM International Conference on Program Comprehension (ICPC)

作者： Zejian Shi Yun Xiong Yao Zhang Zhijie Jiang Jinjing Zhao Lei Wang Shanshan Li Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Peng Cheng Laboratory Shenzhen China School of Computer National University of Defense Technology China National Key Laboratory of Science and Technology on Information System Security China

Contrastive learning has recently been applied to enhancing the BERT-based pre-trained models for code search. However, the existing end-to-end training mechanism cannot sufficiently utilize the pre-trained models due to the limitations on the number and variety of negative samples. In this paper, we propose MoCoCS, a multi-modal momentum contrastive learning method for code search, to improve the representations of query and code by constructing large-scale multi-modal negative samples. MoCoCS increases the number and the variety of negative samples through two optimizations: integrating multi-batch negative samples and constructing multi-modal negative samples. We first build momentum contrasts for query and code, which enables the construction of large-scale negative samples out of a mini-batch. Then, to incorporate multi-modal code information, we build multi-modal momentum contrasts by encoding the abstract syntax tree and the data flow graph with a momentum encoder. Experiments on CodeSearchNet with six programming languages demonstrate that our method can further improve the effectiveness of pre-trained models for code search.

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DPFuzzer: Discovering Safety Critical Vulnerabilities for Drone Path Planners

DPFuzzer: Discovering Safety Critical Vulnerabilities for Dr...

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International Conference on Software Engineering (ICSE)

作者： Yue Wang Chao Yang Xiaodong Zhang Yuwanqi Deng JianFeng Ma State Key Laboratory of Integrated Services Networks (ISN) Xidian University China Shaanxi Key Laboratory of Network and System Security Xidian University Xi'an China School of Cyber Engineering Xidian University China School of Computer Science and Technology Xidian University China

ISBN: (数字)9798331505691

ISBN: (纸本)9798331505707

State-of-the-art drone path planners enable drones to autonomously travel through obstacles in GPS-denied, uncharted, cluttered environments. However, our investigation shows that path planners fail to maneuver drones correctly in specific scenarios, leading to incidents such as collisions. To minimize such risks, drone path planners should be tested thoroughly against diverse scenarios before deployment. Existing research for testing drones to uncover safety-critical vulnerabilities is only focused on flight control programs and is limited in the capability to generate diverse obstacle scenarios for testing drone path planners. In this work, we propose DPFuzzer, an automated framework for testing drone path planners. DPFuzzer is an evolutionary algorithm (EA) based testing framework. It aims to uncover vulnerabilities in drone path planners by generating diverse critical scenarios that can trigger vulnerabilities. To better guide the critical scenario generation, we introduce Environmental Risk Factor (ERF), a metric we propose, to abstract potential safety threats of scenarios. We evaluate DPFuzzer on state-of-the-art drone path planners and the experimental result shows that DPFuzzer can effectively find diverse vulnerabilities. Additionally, we demonstrate that these vulnerabilities are exploitable in the real world.

关键词： Measurement Evolutionary computation Fuzzing Safety Scenario generation Fuels Drones Testing Software engineering Aerospace control

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A Virtual Network Mapping Method Based on Compound Particle Swarm Optimization 7

A Virtual Network Mapping Method Based on Compound Particle ...

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7th International Conference on Cyber Security and Information Engineering, ICCSIE 2022

作者： Huang, Jianhua Tang, Yunlong Wei, Yongjun Wang, Huan Zhang, Haifeng Zhang, Bing School of Computer Science and Technology School of Software Guangxi University of Science and Technology Liuzhou Key Laboratory of Big Data Intelligent Processing and Security Guangxi Education System Network Security Monitoring Center Liuzhou China Liuzhou Railway Vocational Technical College Liuzhou China

ISBN: (纸本)9781665455749

Aiming at problems of low credibility of results and low accuracy of the scheme in the mapping process of virtual networks. It is suggested to use a composite particle swarm optimization approach for virtual network mapping. Firstly, to derive the maximum number of virtual cyberspaces, we construct the virtual network model and the physical network model;secondly, resource limitations are used to build the node mapping and link mapping models, and once more, the mapping scheme of the virtual network on the physical network is solved using the composite particle swarm algorithm to improve the acceptance rate of virtual network requests. In simulation experiments, we compare our established algorithm with two algorithms, D-ViNE-SP and VNE-R-PSO, in two dimensions, and the results show that our proposed VNE-MM-PSO mapping method improves the acceptance rate of network requests by 8% and also improves on the benefit-cost ratio. © 2022 IEEE.

关键词： Particle swarm optimization (PSO)

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An AoI-Guaranteed Sensor Data Collection Strategy for RIS-assisted UAV Communication system

An AoI-Guaranteed Sensor Data Collection Strategy for RIS-as...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Yang, Bowen Yu, Yao Li, Jianqi Xu, Tongze Deng, Der-Jiunn Chin, Hui-Hsin Li, Yonghui Northeastern University School of Computer Science and Engineering Shenyang China National Changhua University of Education Department of Computer Science and Information Engineering China Overseas Chinese University Department of Information Technology China University of Sydney School of Electrical and Information Engineering Sydney Australia Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China

ISBN: (纸本)9798350345384

The recently proposed concept of Age of Information (AoI) measures the freshness of the sensor data sampled by remote Internet of Things (IoT) devices, which is an important indicator for the smart city. Unmanned Aerial Vehicles (UAV) communication can significantly reduce the sensor data's AoI due to its high maneuverability. However, the UAV's high energy consumption remains a challenging issue. In this paper, we propose a Reconfigurable Intelligent Surface (RIS) assisted UAV data collection framework to minimize UAV energy consumption. By adjusting the phase of the RIS, the UAV can collect more sensor data simultaneously. A RIS phase alignment method is introduced to reduce the complexity of RIS phase solving. The IoT devices are clustered to discretize UAV trajectory. The optimal clustering scheme to maximize the AoI suppression efficiency is found by adjusting the RIS phase alignment points continuously. Therefore, the AoI requirement can be achieved with a shorter UAV flight distance. Finally, the UAV trajectory is solved by the Deep Q network (DQN) algorithm to guarantee the long-term AoI requirement. Simulation results validate the effectiveness of the proposed method. © 2023 IEEE.

关键词： Maneuverability

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Improving Personality Consistency in Conversation With Commonsense Knowledge

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2024年 33卷 262-271页

作者： Yifan Li Shixuan Fan Wei Wei Xianling Mao Kaihe Xu Dangyang Chen Alibaba Company Ltd. Zhejiang China Cognitive Computing and Intelligent Information Processing (CCIIP) Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Beijing Institute of Technology Beijing China Ping An Property and Casualty Insurance Company of China Ltd. Shenzhen China

Maintaining persona consistency is crucial for an open-domain dialogue system. Existing methods focus on extracting user persona from historical context to guide persona-consistent response generation. Actually, the historical conversations are usually noisy and uninformative, and thus the target response generation is challenging, as well as easily falling into sub-optimization, especially for the well-known semantic gap between the potential persona information in the history and the target response, which can be bridged by dynamically injecting commonsense knowledge for inference. Hence, in this paper, we propose a persona-consistent dialogue model, consisting of a unified word-concept representing framework and a commonsense reasoning component, to reduce the semantic distance between persona and response. Extensive experiments demonstrate that our proposed model achieves significant enhancements in persona coherence.

关键词： Semantics Commonsense reasoning History Context modeling Games Knowledge graphs Chatbots Speech processing Oral communication Decoding

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Analog Circuit Design of Normalized Bistable Stochastic Resonance Model and Its Application in Weak Signal Processing

Analog Circuit Design of Normalized Bistable Stochastic Reso...

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International Conference on Networking and Network Applications (NaNA)

作者： Jin Liu Zan Li Qiguang Miao Li Yang Peihan Qi Danyang Wang School of Computer Science and Technology Xidian University Xi’an China State Key Laboratory of Integrated Services Networks Xidian University Xi’an China

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

The concept of stochastic resonance (SR) describes a counterintuitive phenomenon in which a weak signal can be enhanced by appropriate intensity of stochastic noise in a nonlinear bistable system, and it provides a new thought for weak signal processing. However, the classical SR theory has demonstrated that the resonance phenomenon occurs on condition that signal and noise obeying adiabatic approximation, i.e., signal with very low frequency and background gaussian white noise of variance less than one. Aiming at this, a normalized bistable stochastic resonance (NBSR) model can be built under adiabatic approximation condition. On this basis, using NBSR model as the reference model, SR technique can further be extended into practical weak signal processing in nonadiabatic approximation condition by linear conversion method. Considering key role of the NBSR model in building link between adiabatic approximation SR theory and it’s expansion into practical application, an analog circuit design of NBSR model is studied, which provides a solution to signal oversampling problem in NBSR system digital realization. Furthermore, both numeric and circuit simulation experiments are given to validate the correctness of the NBSR system analog circuit design.

关键词： Stochastic resonance Circuit simulation Buildings Modulation Symbols Stochastic processes Analog circuits

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Accelerating Backward Aggregation in GCN Training with Execution Path Preparing on GPUs

arXiv

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arXiv 2022年

作者： Xu, Shaoxian Shao, Zhiyuan Yang, Ci Liao, Xiaofei Jin, Hai The National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China

The emerging Graph Convolutional Network (GCN) has been widely used in many domains, where it is important to improve the efficiencies of applications by accelerating GCN trainings. Due to the sparsity nature and exploding scales of input real-world graphs, state-of-the-art GCN training systems (e.g., GNNAdvisor) employ graph processing techniques to accelerate the message exchanging (i.e. aggregations) among the graph vertices. Nevertheless, these systems treat both the aggregation stages of forward and backward propagation phases as all-active graph processing procedures that indiscriminately conduct computations on all vertices of an input graph. In this paper, we first point out that in a GCN training problem with a given training set on an input graph, its aggregation stages of backward propagation phases (called as backward aggregations in this paper) can be equivalently converted to partially-active graph processing procedures, which conduct computations on only partial vertices of the input graph. By leveraging such a finding, we propose an execution path preparing method that collects and coalesces the graph data used during different training layers of backward aggregations, and constructs their corresponding sub-graphs (called as execution paths in this paper) as inputs to conduct the backward training on GPUs. Further, we propose a structural-aware strategy for the execution paths to compute their optimal group sizes, so as to gain as high as possible performances on GPUs during the backward aggregations. The experiment results by conducting GCN training in typical real-world graphs show that compared with GNNAdvisor, our approach improves the performance of backward aggregations by up to 5.68x on NVIDIA P100 GPU, and up to 6.57x on NVIDIA V100S GPU. Copyright © 2022, The Authors. All rights reserved.

关键词： Graphics processing unit

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GCD-Sampling: A General Cross-scale Decoupled Sampling for Point Cloud 39

GCD-Sampling: A General Cross-scale Decoupled Sampling for P...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Dai, Tao Wang, Yanzi Xiong, Jianyu Zha, Yaohua Xia, Shu-Tao Zhu, Zexuan College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Tsinghua Shenzhen International Graduate School Tsinghua University China Shenzhen City Key Laboratory of Embedded System Design Shenzhen China

ISBN: (纸本)157735897X

Sampling strategy (e.g., fixed farthest point sampling) of point cloud has been an essential step for developing practical solutions in 3D computer vision tasks. Previous fixed sampling is simple, but suffer from suboptimal performance for downstream tasks. To adapt to target networks properly, adaptive sampling methods with trainable parameters have been recently developed to enhance the performance. However, existing adaptive sampling methods still suffer from the over-coupling problem of target network, and thus become model-specific, which limits their practical applications. To address this issue, we propose a novel general cross-scale decoupled sampling method (GCD-sampling) for point cloud, which consists of original feature cache, cross-scale feature fusion and convex combination learning for better feature extraction. To reduce the coupling relationship with the target task network, our method only utilizes the point cloud coordinates as the input and output of itself. Besides, we introduce an arbitrary scale structure to enable parameter sharing across multi-scale sampling in point cloud networks. Extensive experiments on different architectures demonstrate the effectiveness of our method over other existing adaptive sampling methods. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Feature extraction

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A Node Confidence Determination Based Moving Target Defense Strategy for SDN

A Node Confidence Determination Based Moving Target Defense ...

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International Conference on Cyber-Enabled Distributed computing and Knowledge Discovery, CyberC

作者： Shimin Sun Guowei Xu Li Han Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Software Tiangong Tianjin China School of Computer Science and Engineering Tianjin University of Technology National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China

With the rapid development of Internet technology, network assets have become an indispensable part of modern society. Especially in the face of high-value network assets, the attacker is faced with paying a greater cost to study the network structure. Due to the unknown attacker and attack means, the attacker has an advantage in the attack and defense situation. The moving target defense technique can flexibly change the static characteristics of the network attack surface and increase the difficulty for the attacker to detect the network, thus achieving the purpose of enhancing network security. Based on this technique, this paper proposes a defense strategy based on node confidence. It puts forward stricter constraints on the differential selection of address/port hopping paths, dynamically selects the hopping objects according to the node’s confidence, and implements the defense strategy of differential complexity. This further increases the network’s uncertainty and the difficulty for the attacker to detect the network structure. The experimental results show that in the path differentiation comparison, the maximum improvement is about 18% compared to the Dynamic Random Route Hopping strategy. In the CPU load experiment, the CPU load rate is reduced by about 4.625% on average compared to the Double Address Hopping strategy. Moreover, for larger hopping period, the proposed strategy also performs well in terms of CPU load rate. The proposed strategy also exhibits good performance in terms of CPU load ratio for large hopping cycles.

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