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International Conference on networking and network Applications (NaNA)

作者： Yichuan Wang Peipei Li Xin Song Yeqiu Xiao Xiaoxue Liu School of computer science and engineering Xi’an University of technology Xi’an China Shanxi Key Laboratory for Network Computing and Security Technology

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

In recent years, system vulnerability mining techniques have emerged in an endless stream. At present, the existing vulnerability mining techniques are mainly to analyze the source code or monitor and analyze the behavior in order to find security vulnerabilities. Most of these techniques require the configuration of the operating environment. Based on the mapping function defined in the network in this paper, a matroid based on system services and vulnerability points is established by utilizing the marking of the number of function set elements in the mapping function. The existence of vulnerable points in the system is verified by analyzing the properties of this matroid. The basic characteristics of the circuit, base and rank of the established matroid are given, and the properties such as the closure of the matroid are analyzed. The results show that, under certain circumstances, a change in the number of elements in the set of services does not lead to a change in the number of vulnerable points distributed in these services. Using this method, probability analysis of system vulnerability distribution can be performed, which is important for the discovery of network security threats.

关键词： Source coding network security Monitoring

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A Novel Multipath Data Transmission Method with Traffic Obfuscation

A Novel Multipath Data Transmission Method with Traffic Obfu...

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International Conference on networking and network Applications (NaNA)

作者： Xinhong Hei Jianfeng Chen Yichuan Wang Xin Song Lei Zhu School of Computer Science and Engineering Xi’an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

With the rapid progress of network communication technology, the issues of user identity and privacy are critical. However, there are many disadvantages in traditional single-path data transmission, for example, the link is susceptible to monitoring and the traffic feature can be easily analyzed. To solve this issue, this paper introduces a multipath data transmission strategy with traffic obfuscation. First, we design a multipath traffic transmission scheme that divides traffic into multiple sub streams and transmits them through multiple paths. The ability of the communication system to resist surveillance and traffic analysis is enhanced by this approach. Second, we effectively confused multipath traffic by changing the size of the data packets, controlling the delay of multipath transmission, and adding redundant traffic. Through experiments, it has been shown that in this advanced multipath transmission strategy, there is no significant difference in the traffic features generated by each node. The effectiveness of the proposed method in obfuscating communication traffic is demonstrated by this.

关键词： Privacy Surveillance Resists Traffic control Delays Data communication Streams

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Hyperspectral Band Selection Based on Classification Reward Adjustment

Hyperspectral Band Selection Based on Classification Reward ...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Jing Hu Jiawei Liang Yunfei Ju Minghua Zhao Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an University of Technology Xi'an China

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Hyperspectral image band selection is a crucial link in image processing. It is necessary to screen out bands with rich information and low correlation, to achieve data dimensionality reduction and retain key information, thereby improving image processing efficiency. Aiming at the differences in reflectance characteristics of targets in different regions in hyperspectral images and the strong correlation between bands, this paper proposes a gradient-guided spatial-spectral weighted hyperspectral band selection method. Firstly, gradient information is introduced to enhance the edge texture features of different targets. Then, the entropy rate superpixel segmentation algorithm is used to segment the first principal component of the image and divide multiple uniform regions. Secondly, to more comprehensively capture the global relationship between all bands in the image, a multi-graph complementary strategy is proposed to construct a regional similarity graph, aiming to maximize the mutual contribution between superpixels with spatial-spectral similarity. Finally, a unified clustering graph is generated by spectral clustering. After normalized cutting, the band with the least noise information in each sub-cube is selected to form a new band subset. The effectiveness of the proposed method is verified by classification experimental results and data analysis.

关键词： Training Reflectivity Image segmentation Correlation Noise Memory modules Predictive models Smart systems Spatiotemporal phenomena Hyperspectral imaging

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Compiler Differentiation Detection Method for Binary Similarity

Compiler Differentiation Detection Method for Binary Similar...

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International Conference on networking and network Applications (NaNA)

作者： Xiaoxue Liu Xinwei Duan Xinhong Hei Yichuan Wang Xin Song School of computer science and engineering Xi’an University of technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

Compiler differential detection can be performed by detecting the similarity between the binary compiled by the compiler and the ideal compiled file. This article proposes a javac compiler differentiation detection system based on binary file similarity: we extract opcode sequences from binary files, use BERT to extract features of the opcodes, and finally obtain the similarity score by calculating the distance between vectors. Under the premise of ensuring the retrieval speed requirements, this algorithm is superior to most existing models in accuracy, with the accuracy rate of $99.7 \%$. It shows that the method in this article can effectively detect whether two binary files are similar. This method will have a good effect on detecting whether the javac compiler has been invaded by malicious code in the future.

关键词： Accuracy Limiting Source coding Semantics Neural networks Feature extraction Malware

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Research on Code Virtualization Methods for Cloud Applications

Research on Code Virtualization Methods for Cloud Applicatio...

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International Conference on networking and network Applications (NaNA)

作者： Zhoukai Wang Zuoyan Xu Yaling Zhang Xin Song Yichuan Wang School of computer science and engineering Xi’an University of technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

With the vigorous development of technologies such as the Internet, mobile devices, and the Internet of Things, cloud services have gained a crucial position in our life and have become an essential part of daily operations for modern enterprises and organizations. However, existing cloud application security solutions have failed to address the problem of runtime binary code security, focusing only on vulnerabilities inherent to the applications themselves. Code virtualization, also known as virtualization obfuscation, is a technique that protects software from malicious analysis by obfuscating the code. It hides the code’s control flow and data flow, thereby preventing the code from being decompiled. In this paper, we introduce a code virtualization method, VMENP, which splits the code into multiple modules and uses polymorphic and modular encryption mechanisms to secure binary code at runtime. Specifically, VMENP divides the protected program into several modules, designs a virtual instruction set along with its corresponding interpreter, encrypts each instruction individually after converting it into intermediate representations, and then encrypts it again at the basic block level. After compiling the segmented code into polymorphic bytecode sequences, it embeds the virtual code’s interpreter into the IR. During runtime, the protected code is dynamically decrypted and executed by the basic block level, with each basic block’s storage and execution in memory in a short time window. VMENP identifies functions annotated in the source code to locate the functions that need protection. This paper uses a large amount of $\mathrm{C} / \mathrm{C}++$ code to perform tests in a real cloud environment. Experimental results show that VMENP achieves a good balance between the granularity of protection and the performance overhead of the protected programs, while successfully protecting $\mathrm{C} / \mathrm{C}++$ code and programs running on X86/64 and ARM32/64 architectures

关键词： Cloud computing Codes Runtime Instruction sets Reverse engineering Computer architecture Binary codes

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Formal Modeling and Defense Methods for 5G network Endpoint Access Denial of Service Attacks

Formal Modeling and Defense Methods for 5G Network Endpoint ...

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2023 International Conference on networking and network Applications, NaNA 2023

作者： Zhang, Tong Wei, Wei Wang, Yichuan Deng, Xi Zhu, Lei Ji, Wenjiang 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 School of Computer Science Xi'an Shiyou University Xi'an China

ISBN: (纸本)9798350327380

5G terminal access denial-of-service attack is one of the more common types of 5G signaling network attacks. It manifests itself as a large number of abnormal access requests occupying core network resources. Normal requests cannot obtain resources, resulting in unstable network services, and users will be unable to connect or drop connections, eventually leading to frequent terminal takeoffs and landings. This paper proposes a formal modeling scheme for a 5G terminal access denial-of-service attack. The attack principle is analyzed, a defense scheme is designed and modeled, and analyzed, and then quantitative experiments are conducted, and the experimental results show the effectiveness of the defense scheme. © 2023 IEEE.

关键词： network security

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Deep Reinforcement-Learning-Based Adaptive Traffic Signal Control with Real-Time Queue Lengths

Deep Reinforcement-Learning-Based Adaptive Traffic Signal Co...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Sun, Qi-Wei Han, Shi-Yuan Zhou, Jin Chen, Yue-Hui Yao, Kang University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China Shandong Provincial Network Security and Infomationization Technology Center Jinan250002 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

The reinforcement learning (RL) with deep neural network, as a data-driven approach, is promising for adaptive traffic signal control (ATSC) in traffic scenarios. The majority of the existing studies focus on designing efficient agents and policy optimization for ATSC, but neglect to observe more detailed states of the environment. In this paper, an adaptive traffic signal control strategy, named as A2C RTQL, is proposed for scheduling the traffic signal in an intersection, by combining the real-time lane-based queue lengths with deep RL agent. First, the Lighthill-Whitham-Richards (LWR) shockwave theory is employed for obtaining the real-time queue lengths in each lane. After that, by defining the obtained queue lengths as the inputs, A2C RTQL strategy is designed for traffic signal control based on the advanced actor-critic (A2C) agent, where the lanes are divided into multiple parallel environments based on the phases of traffic signal. Simulation results demonstrate the optimality and efficiency of the proposed strategy compared with other methods in SUMO under simulated peak-hour traffic dynamics. © 2022 IEEE.

关键词： Reinforcement learning

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Riemannian Complex Matrix Convolution network for PolSAR Image Classification

arXiv

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arXiv 2023年

作者： Shi, Junfei Wang, Wei Jin, Haiyan Nie, Mengmeng Ji, Shanshan Department of Computer Science and Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of Technology Xi’an China The Department of Computer Science and Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of Technology Xi’an China

Recently, deep learning methods have achieved superior performance for Polarimetric Synthetic Aperture Radar(PolSAR) image classification. Existing deep learning methods learn PolSAR data by converting the covariance matrix into a feature vector or complex-valued vector as the input. However, all these methods cannot learn the structure of complex matrix directly and destroy the channel correlation. To learn geometric structure of complex matrix, we propose a Riemannian complex matrix convolution network for PolSAR image classification in Riemannian space for the first time, which directly utilizes the complex matrix as the network input and defines the Riemannian operations to learn complex matrix’s features. The proposed Riemannian complex matrix convolution network considers PolSAR complex matrix endowed in Riemannian manifold, and defines a series of new Riemannian convolution, ReLu and LogEig operations in Riemannian space, which breaks through the Euclidean constraint of conventional networks. Then, a CNN module is appended to enhance contextual Riemannian features. Besides, a fast kernel learning method is developed for the proposed method to learn class-specific features and reduce the computation time effectively. Experiments are conducted on three sets of real PolSAR data with different bands and sensors. Experiments results demonstrates the proposed method can obtain superior performance than the state-of-the-art methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Image classification

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Wrong-of-Thought: An Integrated Reasoning Framework with Multi-Perspective Verification and Wrong Information

Wrong-of-Thought: An Integrated Reasoning Framework with Mul...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zhang, Yongheng Chen, Qiguang Zhou, Jingxuan Wang, Peng Si, Jiasheng Wang, Jin Lu, Wenpeng Qin, Libo School of Computer Science and Engineering Central South University China Research Center for SCIR Harbin Institute of Technology Harbin China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China Yunnan University Kunming China

ISBN: (纸本)9798891761681

Chain-of-Thought (CoT) has become a vital technique for enhancing the performance of Large Language Models (LLMs), attracting increasing attention from researchers. One stream of approaches focuses on the iterative enhancement of LLMs by continuously verifying and refining their reasoning outputs for desired quality. Despite its impressive results, this paradigm faces two critical issues: (1) Single verification method: The current paradigm relies solely on a single verification method. (2) Wrong Information Ignorance: The traditional paradigm directly ignores wrong information during reasoning and refines the logic paths from scratch each time. To address these challenges, we propose Wrong-of-Thought (WoT), which includes two core modules: (1) Multi-Perspective Verification: A multi-perspective verification method for accurately refining the reasoning process and result, and (2) Wrong Information Utilization: Utilizing wrong information to alert LLMs and reduce the probability of LLMs making same mistakes. Experiments on 8 popular datasets and 5 LLMs demonstrate that WoT surpasses all previous baselines. In addition, WoT exhibits powerful capabilities in difficult computation tasks. © 2024 Association for Computational Linguistics.

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Unleashing the Potential of Decentralized Federated Learning in Healthcare: A Comprehensive Survey on Insights and Challenges 9

Unleashing the Potential of Decentralized Federated Learning...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Cheng, Haibo Liu, Wenjian Qu, Youyang Fu, Kexue Wang, Qinyuan City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

Federated learning (FL) is a distributed learning method that reduces data transmission and privacy risks. However, the existence of a central server may become a target of attackers, resulting in an increased risk of user privacy leakage. The emergence of Decentralized Federated Learning (DFL) is posited to enhance data privacy and system reliability by reducing reliance on central servers and enhancing local data control, especially in the medical field, where personal health data is protected. This research investigates the application of (DFL) in the medical domain, focusing on its limitations. This study employs a systematic literature review, adhering to PRISMA guidelines, to comprehensively understand DFL's current landscape and challenges in healthcare. The key findings highlight four principal obstacles faced by DFL: security and privacy, communication efficiency, data and model heterogeneity, and incentive mechanisms. It also explores the potential for DFL to facilitate personalized healthcare treatments by large datasets distributed across various medical institutions. This research fills a significant void by systematically reviewing DFL technologies in healthcare. It provides insights into applications, challenges, and future directions for participants seeking secure and efficient healthcare data handling. © 2024 IEEE.

关键词： Federated learning

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