检索结果-内蒙古大学图书馆

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

作者： Qu, Junpeng Du, Gaolei Sun, Yunge Fu, Yong Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Quan Cheng Laboratory Jinan250103 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798350391367

Aiming at the problem that malicious software uses encryption algorithm to hide its own malicious code and avoid detection, a dynamic detection technology based on Pin encryption algorithm is proposed. Use Pin to get the instruction flow of executable file running, then identify the function call in the instruction flow to get the function call framework, find the loop body in the calling function, and then carry out the encryption algorithm instruction detection and encryption algorithm constant detection on the loop body. If the detection passes, it can be considered that a component of the encryption algorithm is found. Then the complete encryption algorithm is found according to the generated function call framework. Experiments show that this method can effectively identify the encryption algorithms hidden in executable files. © 2024 IEEE.

关键词： Encryption algorithms

来源：评论

学校读者我要写书评

暂无评论

Semantic-Enhanced Static Vulnerability Detection in Baseband Firmware 24

Semantic-Enhanced Static Vulnerability Detection in Baseband...

引用

44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Liu, Yiming Zhang, Cen Li, Feng Li, Yeting Zhou, Jianhua Wang, Jian Zhan, Lanlan Liu, Yang Huo, Wei Institute of Information Engineering Cas School of Cyber Security Ucas Beijing China Key Laboratory of Network Assessment Technology Cas China Beijing Key Laboratory of Network Security and Protection Technology China Nanyang Technological University Singapore

ISBN: (纸本)9798400702174

Cellular network is the infrastructure of mobile communication. Baseband firmware, which carries the implementation of cellular network, has critical security impact on its vulnerabilities. To handle the inherent complexity in cellular communication, cellular protocols are usually implemented as message-centric systems, containing the common message processing phase and message specific handling phase. Though the latter takes most of the code (99.67%) and exposed vulnerabilities (74%), it is rather under-studied: existing detectors either cannot sufficiently analyze it or focused on analyzing the former phase. To fill this gap, we proposed a novel semantic-enhanced static vulnerability detector named BVFinder focusing on message specific phase vulnerability detection. Generally, it identifies a vulnerability by locating whether a predefined sensitive memory operation is tainted by any attacker-controllable input. Specifically, to reach high automation and preciseness, it made two key improvements: a semantic-based taint source identification and an enhanced taint propagation. The former employs semantic search techniques to identify registers and memory offsets that carry attacker-controllable inputs. This is achieved by matching the inputs to their corresponding message and data types using textual features and addressing patterns within the assemblies. On the other hand, the latter technology guarantees effective taint propagation by employing additional indirect call resolution algorithms. The evaluation shows that BVFinder outperforms the state-of-the-art detectors by detecting three to four times of amount of vulnerabilities in the dataset. Till now, BVFinder has found four zero-day vulnerabilities, with four CVEs and 12,410 USD bounty assigned. These vulnerabilities can potentially cause remote code execution to phones using Samsung shannon baseband, affecting hundreds of millions of end devices. © 2024 ACM.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

SEED: Accelerating Reasoning Tree Construction via Scheduled Speculative Decoding 31

SEED: Accelerating Reasoning Tree Construction via Scheduled...

引用

31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Zhenglin Wu, Jialong Lai, Yilong Zhang, Congzhi Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

ISBN: (纸本)9798891761964

Large Language Models (LLMs) demonstrate remarkable emergent abilities across various tasks, yet fall short of complex reasoning and planning tasks. The tree-search-based reasoning methods address this by encouraging the exploration of intermediate steps, surpassing the capabilities of chain-of-thought prompting. However, significant inference latency is introduced due to the systematic exploration and evaluation of multiple thought paths. This paper introduces SEED, a novel and efficient inference framework to improve both runtime speed and GPU memory management concurrently. Based on a scheduled speculative execution, SEED efficiently handles multiple iterations for thought generation and state evaluation, leveraging a rounds-scheduled strategy to manage draft model dispatching. Extensive experimental evaluations on three reasoning datasets demonstrate the superior speedup performance of SEED. © 2025 Association for Computational Linguistics.

关键词： Memory management

来源：评论

学校读者我要写书评

暂无评论

An Encryption Algorithm Cycle Identification Method Based on Bit Execution 9

An Encryption Algorithm Cycle Identification Method Based on...

引用

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

作者： Sun, Yunge Du, Gaolei Qu, Junpeng Fu, Yong 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 Jinan250014 China Quan Cheng Laboratory Jinan250103 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798350391367

Ransomware uses symmetric encryption algorithms to lock user files to extort money. This paper proposes a cryptographic algorithm loop identification method based on bit-precise execution, aiming to solve the problem of identifying unknown loops. The structure of the loop body of encryption algorithms is fixed and usually includes nonlinear operations and linear operations. This article converts the loop body in the unknown software into a set of Boolean formulas, and converts the malware into a register data flow through the symbolic executor, thereby converting it into a set of Boolean formulas for execution. Execute known encryption algorithms with bit accuracy and establish a Boolean formula input-output relationship library for cryptographic components. The two sets of Boolean formulas are passed through a designed input matrix to achieve the Boolean formula mapping and input variable mapping of the confirmation algorithm, which can effectively judge the encryption algorithms in some malware. © 2024 IEEE.

关键词： Encryption algorithms

来源：评论

学校读者我要写书评

暂无评论

An automated construction method for symmetric encryption round function models based on dynamic analysis 9

An automated construction method for symmetric encryption ro...

引用

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

作者： Du, Gaolei Sun, Yunge Qu, Junpeng Fu, Yong 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 Jinan250014 China Quan Cheng Laboratory Jinan250103 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798350391367

With the deepening of informatization, the incidence of cybercrimes utilizing malicious software for attacks is on the rise. This paper addresses the detection issue of symmetric encryption in malicious software from the perspective of cryptography, proposing an automated method for constructing a symmetric encryption round function model based on dynamic analysis. This method tracks data flow at the granularity of registers to characterize Directed Acyclic Graph (DAG) construction of round function models. Initially, dynamic execution traces of binary files are captured using Intel Pin, identifying loops within them. Data flow tracking at the register level within loops is performed, creating DAG, and analyzing whether the loops exhibit characteristics of symmetric cipher round functions, thus constructing the round function model. This approach enables the detection of symmetric encryption within binary files and the construction of round function models, offering a novel solution for fine-grained symmetric cipher recognition and laying a foundation for detecting malicious software containing symmetric encryption. © 2024 IEEE.

关键词： Data flow graphs

来源：评论

学校读者我要写书评

暂无评论

Blockchain-Based Data Acquisition with Privacy Protection in UAV Cluster network

引用

computer Modeling in Engineering & Sciences 2023年第10期137卷 879-902页

作者： Lemei Da Hai Liang Yong Ding Yujue Wang Changsong Yang Huiyong Wang Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information SecurityGuilin University of Electronic TechnologyGuilin541004China Cyberspace Security Research Center Pengcheng LaboratoryShenzhen518055China School of Mathematics and Computing Science Guilin University of Electronic TechnologyGuilin541004China

The unmanned aerial vehicle(UAV)self-organizing network is composed of multiple UAVs with autonomous capabilities according to a certain structure and scale,which can quickly and accurately complete complex tasks such as path planning,situational awareness,and information *** to the openness of the network,the UAV cluster is more vulnerable to passive eavesdropping,active interference,and other attacks,which makes the system face serious security *** paper proposes a Blockchain-Based Data Acquisition(BDA)scheme with privacy protection to address the data privacy and identity authentication problems in the UAV-assisted data acquisition *** UAV cluster has an aggregate unmanned aerial vehicle(AGV)that can batch-verify the acquisition reports within its administrative *** successful verification,AGV adds its signcrypted ciphertext to the aggregation and uploads it to the blockchain for *** are two chains in the blockchain that store the public key information of registered entities and the aggregated reports,*** security analysis shows that theBDAconstruction can protect the privacy and authenticity of acquisition data,and effectively resist a malicious key generation center and the public-key substitution *** also provides unforgeability to acquisition reports under the Elliptic Curve Discrete Logarithm Problem(ECDLP)*** performance analysis demonstrates that compared with other schemes,the proposed BDA construction has lower computational complexity and is more suitable for the UAV cluster network with limited computing power and storage capacity.

关键词： Unmanned aerial vehicle cluster network certificateless signcryption certificateless signature batch verification source authentication data privacy blockchain

来源：评论

学校读者我要写书评

暂无评论

DMF: A Deep Multimodal Fusion-Based network Traffic Classification Model

引用

computers, Materials & Continua 2025年第5期83卷 2267-2285页

作者： Xiangbin Wang Qingjun Yuan Weina Niu Qianwei Meng Yongjuan Wang Chunxiang Gu Henan Key Laboratory of Network Cryptography Technology Information Engineering UniversityZhengzhou450001China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China

With the rise of encrypted traffic,traditional network analysis methods have become less effective,leading to a shift towards deep learning-based *** these,multimodal learning-based classification methods have gained attention due to their ability to leverage diverse feature sets from encrypted traffic,improving classification ***,existing research predominantly relies on late fusion techniques,which hinder the full utilization of deep features within the *** address this limitation,we propose a novel multimodal encrypted traffic classification model that synchronizes modality fusion with multiscale feature ***,our approach performs real-time fusion of modalities at each stage of feature extraction,enhancing feature representation at each level and preserving inter-level correlations for more effective *** continuous fusion strategy improves the model’s ability to detect subtle variations in encrypted traffic,while boosting its robustness and adaptability to evolving network *** results on two real-world encrypted traffic datasets demonstrate that our method achieves a classification accuracy of 98.23% and 97.63%,outperforming existing multimodal learning-based methods.

关键词： Deep fusion intrusion detection multimodal learning network traffic classification

来源：评论

学校读者我要写书评

暂无评论

Reliability Assessment of a New General Matching Composed network

引用

China Communications 2024年第2期21卷 245-257页

作者： Zhengyuan Liang Junbin Liang Guoxuan Zhong Guangxi Key Laboratory of Multimedia Communications and Network Technology School of ComputerElectronics and InformationGuangxi UniversityNanning 530004China Jiangxi Engineering Laboratory of IoT Technologies for Crop Growth Ji’an 343000China

The reliability of a network is an important indicator for maintaining communication and ensuring its stable operation. Therefore, the assessment of reliability in underlying interconnection networks has become an increasingly important research issue. However, at present, the reliability assessment of many interconnected networks is not yet accurate,which inevitably weakens their fault tolerance and diagnostic capabilities. To improve network reliability,researchers have proposed various methods and strategies for precise assessment. This paper introduces a novel family of interconnection networks called general matching composed networks(gMCNs), which is based on the common characteristics of network topology structure. After analyzing the topological properties of gMCNs, we establish a relationship between super connectivity and conditional diagnosability of gMCNs. Furthermore, we assess the reliability of g MCNs, and determine the conditional diagnosability of many interconnection networks.

关键词： conditional diagnosability interconnection networks network reliability super connectivity

来源：评论

学校读者我要写书评

暂无评论

A Dirty Data Identification Method in Mechanical Equipment Condition Monitoring 10

A Dirty Data Identification Method in Mechanical Equipment C...

引用

10th IEEE Smart World Congress, SWC 2024

作者： Hao, Huijuan Zhao, Hongge Zhu, Sijian Feng, Yue Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China JinanChina

ISBN: (纸本)9798331520861

The rise of the Industrial Internet of Things (IIOT) has brought new opportunities for the mechanical equipment condition monitoring. The trend towards intelligent and automated production of mechanical equipment has become mainstream. However, in the actual operational environment, mechanical equipment is often mixed with a large amount of dirty data unrelated to equipment failures, which seriously affects equipment fault diagnosis and wastes maintenance resources. To improve the reliability of mechanical equipment health status analysis, this paper proposes a new method for dirty data identification suitable for mechanical equipment condition monitoring. First, utilizing the time-space position encoding (TSPE) to map positional relationships between sequences. Next, designing a time-dimension attention (TDA) mechanism to capture the correlation between multiple temporal attribute features and the temporal dependencies between preceding and subsequent data. Then, building a dirty data identification model using an improved Transformer network and Generative Adversarial network (GAN). Finally, employing an adaptive thresholding method to dynamically set thresholds, determining the degree of data anomalies by comparing anomaly scores with the threshold. The effectiveness of the proposed method are validated through one simulation experiment and one real-world example. The F1 Score of this method on the simulated missing data and drifting data is 0.9874 and 0.9838. On the drive-end and fan-end is 0. 9 7 0 0 and 0.9882 at Case Western Reserve University (CWRU). Compared to other dirty data identification methods in the field, this method has better dirty data identification capability. © 2024 IEEE.

关键词： Reliability analysis

来源：评论

学校读者我要写书评

暂无评论

An Approach to New Bearing Fault Diagnosis Utilizing Deep Transfer Learning 10

An Approach to New Bearing Fault Diagnosis Utilizing Deep Tr...

引用

10th IEEE Smart World Congress, SWC 2024

作者： Hao, HuiJuan Feng, Yue Zhao, HongGe Zhu, SiJian Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Jinan China

ISBN: (纸本)9798331520861

Bearings, serving as crucial components in mechanical equipment, exert a direct influence on the overall performance of the system. Deep transfer learning has garnered significant attention in the realm of intelligent fault diagnosis for machines. Nevertheless, extant deep transfer learning algorithms encounter challenges when detecting newly emerging faults within the target domain. To address this limitation, this study introduces a novel bearing fault diagnosis approach for newly emerging faults, leveraging deep transfer learning principles. Initially, a multi-grained hierarchical feature extraction network is constructed to effectively capture intricate data characteristics. Subsequently, these extracted features are employed to train a fault classifier, thereby establishing a fault diagnosis network grounded in deep transfer learning. This network is capable of discriminating unlabeled new faults from labeled known faults with remarkable precision. Furthermore, an adaptive hierarchical clustering algorithm, informed by the elbow method, is incorporated to categorize the newly emerging faults. The clustered samples are then utilized to train the online sequential extreme learning machine (OS-ELM), facilitating the construction of a new fault diagnosis network tailored for diagnosing these faults in the target domain. The efficacy of the proposed methodology was rigorously verified through experiments executed on both the CWRU and XJTU-SY datasets. The outcomes conclusively demonstrated an accuracy of 9 8. 7 5 % on the CWRU dataset and 9 7. 9 2 % on the XJTU-SY dataset. This model portends immense potential in tackling fault diagnosis transfer scenarios characterized by the emergence of multiple novel faults within the target domain, thereby markedly broadening the applicability of deep transfer learning. © 2024 IEEE.

关键词： Deep Transfer Learning Detection of New Faults Multi-grained Feature Extraction OS-ELM

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：