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IEEE International Conference on Systems, Man and Cybernetics

作者： Menglu Wang Xiaohui Han Peipei Wang Wenbo Zuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Quan Cheng Laboratory Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In the field of cybersecurity, analyzing malicious software or programs is crucial for preventing network attacks. Malicious code often exists in a stripped binary form to thwart analysis, presenting challenges for analysts. This study investigates inferring function names from stripped binary to aid security researchers in analyzing malicious code. We propose PTGFI, a Prompt-based Two-stage Generative framework for Function name Inference. The PTGFI framework transforms the task of inferring function names into a two-stage semantic generation problem. By capturing function descriptions of assembly functions and introducing prompt learning, effective inference of function names is achieved. In experiments, PTGFI outperforms the state-of-the-art model by 2.96 % in precision. Moreover, ablation studies demonstrate the effectiveness of advanced components within the PTGFI framework. We further validate the utility and reliability of function names generated by the PTGFI framework through case studies.

关键词： Analytical models Codes Accuracy Semantics Transforms Malware Reliability computer crime Assembly Cybernetics

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The Power of Bamboo: On the Post-Compromise security for Searchable Symmetric Encryption 30

The Power of Bamboo: On the Post-Compromise Security for Sea...

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30th Annual network and Distributed System security Symposium, NDSS 2023

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

ISBN: (纸本)1891562835

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2023 30th Annual network and Distributed System security Symposium, NDSS 2023. All Rights Reserved.

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Differentially Private Decision Tree Based on Pearson's Correlation Coefficient 11

Differentially Private Decision Tree Based on Pearson's Corr...

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11th International Conference on information Science and Technology, ICIST 2021

作者： Yan, Qi Wang, Jinyan Liu, Songfeng Li, De Guangxi Key Lab of Multi-source Information Mining Security Guangxi Normal University Guilin China School of Computer Science and Information Technology Guangxi Normal University Guilin China

ISBN: (纸本)9781665412667

As one of popular machine learning algorithms, decision trees can be applied to both classification and regression tasks. Many decision tree models are trained and used by some companies and researchers on data obtained from users for business decision. However, the use of decision trees may pose a potential risk to privacy when training data contains sensitive personal information. In this paper, we proposed an effective differentially private decision tree algorithm based on Pearson's correlation coefficient, called Diff-PCCDT. The exponential mechanism is applied to select the best splitting attribute with the absolute value of Pearson's correlation coefficient as quality function at every intermediate node. The laplace noise is added into the true class count of every leaf node to ensure that the decision tree does not leak privacy during classification. Furthermore, a parallel algorithm of Diff-PCCDT over Map-Reduce, called DiffMR-PCCDT, is proposed for big data scenario. The experimental results demonstrate that the Diff-PCCDT algorithm can effectively train differentially private decision tree and its parallel implementation DiffMR-PCCDT can deal with the big data classification problems. © 2021 IEEE.

关键词： Decision trees

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An Improved iBAT-COOP Protocol for Cooperative Diversity in FANETs

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computers, Materials & Continua 2021年第5期67卷 2527-2546页

作者： Shahzad Hameed Qurratul-Ain Minhas Sheeraz Ahmed Shabana Habib Mohammad Kamrul Hasan Muhammad Islam Sheroz Khan Quaid-i-Azam University IslamabadPakistan Iqra National University PeshawarPakistan Network and Communication Technology Lab Center for Cyber SecurityUKMBangiMalaysia Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Electrical Engineering Onaizah College of Engineering&Information TechnologyAl-QassimSaudi Arabia

Flying ad hoc networks(FANETs)present a challenging environment due to the dynamic and highly mobile nature of the *** network topology and uncertain node mobility structure of FANETs do not aim to consider only one path *** different techniques are adopted to address the issues arising in FANETs,from game theory to clustering to channel estimation and other statistical *** approaches mostly employ traditional concepts for problem *** of the novel approaches that provide simpler solutions to more complex problems is to use biologically inspired *** Nature-inspired schemes address cooperation and alliance which can be used to ensure connectivity among network *** such species that resembles the dynamicity of FANETs are *** this paper,the biologically inspired metaheuristic technique of the BAT Algorithm is proposed to present a routing protocol called iBATCOOP(Improved BAT Algorithm using Cooperation technique).We opt for the design implementation of the natural posture of bats to handle the necessary flying ***,we envision the concept of cooperative diversity using multiple relays and present an iBAT-COOP routing protocol for *** paper employs cooperation for an optimal route selection and reflects on distance,Signal to Noise Ratio(SNR),and link conditions to an efficient level to deal with FANET’s *** way of simulations,the performance of iBAT-COOP protocol outperforms BAT-FANET protocol and reduces packet loss ratio,end-to-end delay,and transmission loss by 81%,21%,and 82%***,the average link duration is improved by 25%compared to the BAT-FANET protocol.

关键词： Routing protocols UAVs FANETs iBATCOOP BATCOOP

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Intelligent Contract Vulnerability Detection Method Based on Bic-RL

Intelligent Contract Vulnerability Detection Method Based on...

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Data security and Privacy Protection (DSPP), International Conference on

作者： Shuhui Zhang Changdong Hu Tian Lan LianHai Wang Shujiang Xu Wei Shao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Blockchain technology is praised for its decentralization, anonymity, and tamper-proof nature, but its complex architecture has led to numerous attacks, particularly against smart contracts. These attacks have caused significant economic losses and hindered the development of blockchain technology. Therefore, research on smart contract vulnerability detection methods has significant practical and application value. Existing detection technologies (such as symbolic execution, formal verification, and so on) have limitations, including heavy reliance on contract source code and incomplete feature extraction. This paper proposes a fully automated smart contract vulnerability detection method based on Bic-RL(Smart contracts Detection Based on Bicubic interpolation and Deep Residual Neural networks - Long short-term memory) to address these issues. This method not only simplifies the data processing process but also realizes the complete feature extraction and ensures the high efficiency of detection performance. Performance evaluation shows this method has higher detection accuracy and generalization ability, with an accuracy rate of 84.5%.

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Visual malware classification and detection method based on convolutional neural network

Visual malware classification and detection method based on ...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Hua Huang Hua Sheng Xingwang Han Wenbin Zhang Zilin Zhao Information and Telecommunications Company State Grid Shandong Electric Power Company Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

With the rapid development of Internet technology, leading to the evolution of malware variants, and malicious threats are also increasing. Therefore, rapid and accurate classification of malware becomes crucial. In response to this challenge, this paper proposes a malware classification method based on deep learning and visualization techniques. The method first extracts sample features, then converts malware binary samples into image format, thus transforming abstract binary data into images with visual information. Subsequently, color mapping technique is applied to convert the processed images into color images. Furthermore, to address imbalanced datasets, this paper introduces data augmentation methods to ensure more balanced and comprehensive training of the model across different sample categories. Finally, an improved convolutional neural network architecture is employed for the classification detection of malware families, achieving accurate classification and identification of different malware families through learning and analysis of sample features. To evaluate the effectiveness of the proposed method, experiments are conducted on the Google Code Jam (GCJ) benchmark dataset. The experimental results demonstrate an accuracy rate of 99.29% on the GCJ dataset. Additionally, we also compared the performance of the improved method before and after, and found that the proposed method can provide more comprehensive feature representation and significantly improve classification accuracy and detection efficiency.

关键词： Deep learning Training Visualization Accuracy Color Static analysis Feature extraction Malware Internet Convolutional neural networks

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Symbiosis Rather Than Aggregation: Towards Generalized Federated Learning via Model Symbiosis

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IEEE Internet of Things Journal 2025年

作者： Liu, Yuange Liu, Yuru Zhang, Weishan Zheng, Chaoqun Jiang, Wei Luo, Daobin Qiao, Qiao Meng, Lingzhao Zhao, Hongwei Yang, Su College of Computer Science and Technology Qingdao266580 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 Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan250014 China Information Science Academy of China Electronics Technology Group Corporation Beijing100036 China Fudan University School of Computer Science Shanghai200433 China

Federated Learning (FL) faces significant challenges in scenarios with non-IID (non-independent and identically distributed) data distributions across participating clients. Traditional aggregation-based approaches often struggle with the inherent misalignment between local and global optimization objectives, leadingwhich leads to gradient divergence and suboptimal generalization performance. This paper proposes a novel FL framework that replaces conventional aggregation with a biologically inspired model symbiosis approach called FedSym, which employs a dual-level symbiotic mechanism. Ectosymbiosis performs coarse-grained hierarchical parameter recombinations through random layer-wise model combination, while endosymbiosis enables fine-grained intra-layer parameter fusion through weighted averaging, collectively steering model updates towards flatter loss landscapes. Our theoretical analysis demonstrates that FedSym's convergence rate is O(1T) under non-IID conditions, which matches the convergence properties of FedAvg. Extensive experimentsevaluations across multiple datasets and model architectures show that FedSym achieves substantial improvements over state-of-the-art FL methods, particularly in challenging scenarios with high data heterogeneity, and demonstrates robust performance across varying numbers of participating clients and federation scales. © 2014 IEEE.

关键词： Federated learning

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STAD: Joint Spatial-Temporal Dimension and Channel Correlation for Time Series Anomaly Detection

STAD: Joint Spatial-Temporal Dimension and Channel Correlati...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Mingle Zhou Xingli Wang Delong Han Jin Wan Jiachen Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurately identifying real anomalies and pseudo-anomalies in complex multi-dimensional time series data has been a difficult problem in time series anomaly detection. To solve this problem, this paper proposes a new framework, STAD, that joint temporal and spatial dimensions. This framework guides the model to capture the correlation information between channels It also aims to learn the deep feature representation of sequences by mining potential information in the spatialtemporal dimension. It can effectively distinguish between true and false anomalies by comparing information from spatial and temporal dimensions. STAD identifies and integrates correlated channels by using a correlation aggregation mechanism to join multiple channels and detect *** addition, the KAN mixer designed in this paper can effectively extract features from different spatial locations in the spatial-temporal dimension. Through extensive experiments on several public datasets, STAD demonstrates its superiority in terms of accuracy and robustness.

关键词： Correlation Accuracy Time series analysis Signal processing Feature extraction Robustness Acoustics Speech processing Mixers Anomaly detection

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A Brief Survey on Privacy-Preserving Methods for Graph-Structured Data

A Brief Survey on Privacy-Preserving Methods for Graph-Struc...

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International Conference on Image, Vision and Intelligent Systems, ICIVIS 2021

作者： Zhang, Yunan Wu, Tao Xian, Xingping Xu, Yuqing Electric Power Research Institute CSG Guangzhou China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China School of Cybersecurity and Information Law Chongqing University of Posts and Telecommunications Chongqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9789811669620

As one of the main manifestations of big data, graph-structured data widely exists in various fields such as social networks, smart cities, medical health, and finance, and is characterized by high dimension, nonlinear, scale-free, small world, etc. Extensive graph-structured data provides sufficient data resources for scientific research and commercial applications. However, graph data mining not only reveals the intrinsic value of data, but also brings the risk of privacy disclosure. Therefore, how to protect graph-structured data privacy is of great significance. In this paper, we survey the very recent research development on privacy preserving methods for graph-structured data. We introduce the common sensitive information and the related privacy risks in graph data, and elaborate the background knowledge for privacy inference attack. Then, the privacy inference attack methods and privacy preservation methods for graph-structured data are summarized. Finally, the shortcomings of the current research about graph-structured data privacy preservation and the possible research directions are discussed. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Surveys

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Revisiting the Application of Twin Connected Parallel networksand Regression Loss Functions in Industrial Defect Detection

SSRN

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SSRN 2023年

作者： Su, Zhanzhi Zhou, Mingle Li, Min Zhang, Zekai Zhang, Cheng Xing, Rui Li, Gang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250353 China

Industrial product surface defects can affect the reliability and lifetime of products. However, industrial scenes are complex, with a wide variety of products, and product surface defects often have characteristics such as large-scale variations and tiny targets, which makes it challenging to achieve the expected results in defect detection. To this end, this paper designs a Twin-Connected Parallel network (TCPNet) for high-precision defect detection and recognition. The innovative features of TCPNet include three points: First, a twin-connected parallel network is designed to efficiently extract semantic and texture information of objects through parallel networks, enhance important feature information of different branch networks in real-time, and ensure local-to-global feature extraction. Second, Differential Feature Remodeling Module(DFRM) modules designed. Carry out feature differentiation processing, and reshape the different features in the local feature extraction module to highlight the defective features. Third, Considering the difficulty of regression and model convergence for small objects, this paper designs the Dynamic Variation regression loss function (DVIoU) based on the dynamic distance relationship between the four pairs of vertices of the target frame and the real frame. Finally, this paper proposes an industrial defect dataset for bottlebody and practically applies TCPNet to industrial scenarios, with experimental results reaching SOTA. © 2023, The Authors. All rights reserved.

关键词： Surface defects

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