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CToMP: a cycle-task-oriented memory protection scheme for unmanned systems

science China(Information sciences) 2024年第6期67卷 399-417页

作者： Chengyan MA Ning XI Di LU Yebo FENG Jianfeng MA School of Cyber Engineering Xidian University School of Computer Science and Technology Xidian University Department of Computer and Information Science University of Oregon

Memory corruption attacks(MCAs) refer to malicious behaviors of system intruders that modify the contents of a memory location to disrupt the normal operation of computing systems, causing leakage of sensitive data or perturbations to ongoing processes. Unlike general-purpose systems, unmanned systems cannot deploy complete security protection schemes, due to their limitations in size, cost and *** in unmanned systems are particularly difficult to defend against. Furthermore, MCAs have diverse and unpredictable attack interfaces in unmanned systems, severely impacting digital and physical sectors. In this paper, we first generalize, model and taxonomize MCAs found in unmanned systems currently, laying the foundation for designing a portable and general defense approach. According to different attack mechanisms,we found that MCAs are mainly categorized into two types — return2libc and return2shellcode. To tackle return2libc attacks, we model the erratic operation of unmanned systems with cycles and then propose a cycle-task-oriented memory protection(CToMP) approach to protect control flows from tampering. To defend against return2shellcode attacks, we introduce a secure process stack with a randomized memory address by leveraging the memory pool to prevent Shellcode from being executed. Moreover, we discuss the mechanism by which CTo MP resists the return-oriented programming(ROP) attack, a novel variant of return2libc attacks. Finally, we implement CTo MP on CUAV V5+ with Ardupilot and Crazyflie. The evaluation and security analysis results demonstrate that the proposed approach CTo MP is resilient to various MCAs in unmanned systems with low footprints and system overhead.

关键词： unmanned system memory corruption attack memory protection system security randomized memory address

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SwiftTheft: A Time-Efficient Model Extraction Attack Framework Against Cloud-Based Deep Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 90-100页

作者： Wenbin YANG Xueluan GONG Yanjiao CHEN Qian WANG Jianshuo DONG School of Cyber Science and Engineering Wuhan University School of Computer Science Wuhan University College of Electrical Engineering Zhejiang University

With the rise of artificial intelligence and cloud computing, machine-learning-as-a-service platforms,such as Google, Amazon, and IBM, have emerged to provide sophisticated tasks for cloud applications. These proprietary models are vulnerable to model extraction attacks due to their commercial value. In this paper, we propose a time-efficient model extraction attack framework called Swift Theft that aims to steal the functionality of cloud-based deep neural network models. We distinguish Swift Theft from the existing works with a novel distribution estimation algorithm and reference model settings, finding the most informative query samples without querying the victim model. The selected query samples can be applied to various cloud models with a one-time selection. We evaluate our proposed method through extensive experiments on three victim models and six datasets, with up to 16 models for each dataset. Compared to the existing attacks, Swift Theft increases agreement(i.e., similarity) by 8% while consuming 98% less selecting time.

关键词： Performance evaluation Cloud computing Analytical models Computational modeling Estimation Artificial neural networks Artificial intelligence

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CAED: A Comprehensive Android Emulator Detection Framework with Data Augmentation

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IEEE Internet of Things Journal 2025年第13期12卷 22847-22861页

作者： Yan, Ran Niu, Weina Hou, Qinsheng Su, YuChi Gong, Jiacheng Zhang, Xiaosong School of Computer Science and Engineering Institute for Cyber Security Chengdu611731 China Shanghai Jiao Tong University School of Cyber Science and Engineering China

Anti-emulation is crucial for Android and IoT security as it helps apps determine whether they are running on a real mobile device or in an emulation environment. This prevents apps from being analyzed, debugged, or reverse-engineered in emulators, ultimately stopping criminals from making illegal profits. Current emulator detection methods cannot balance accuracy, universality, robustness, and compatibility. Their universality is often hindered by limited data diversity and accessibility. To address these issues, we propose the Comprehensive Android Emulator Detection (CAED) framework. The Preprocessing Module of CAED collects and normalizes data from both phones and emulators. We propose the first data augmentation method for emulator detection, EDA-GAN, which is tailored to the characteristics of our data and effectively enhances data diversity. The classifier module MFBoost employs an adaptive imputation algorithm and multiple Classification and Regression Trees (CART) for precise classification. Experiments on 324 devices show that CAED improves detection rate by at least 12.5% and up to 44.71% over state-of-the-art (SOTA) methods. The EDA-GAN data augmentation method boosts classifier accuracy, achieving a performance of up to 99.62%. Additionally, CAED’s unique loss function and imputation algorithm enhance the robustness and compatibility of CAED, with a 24% smaller accuracy drop than other methods when features are modified or unavailable. This study presents the CAED framework as an effective solution for protecting apps against real-world security threats in Android and IoT environments. © 2014 IEEE.

关键词： Crime

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An end-to-end network for co-saliency detection in one single image

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science China(Information sciences) 2023年第11期66卷 5-22页

作者： Yuanhao YUE Qin ZOU Hongkai YU Qian WANG Zhongyuan WANG Song WANG School of Cyber Science and Engineering Wuhan University School of Computer Science Wuhan University Department of Electrical Engineering and Computer Science Cleveland State University Department of Computer Science and Engineering University of South Carolina

Co-saliency detection within a single image is a common vision problem that has not yet been well addressed. Existing methods often used a bottom-up strategy to infer co-saliency in an image in which salient regions are firstly detected using visual primitives such as color and shape and then grouped and merged into a co-saliency map. However, co-saliency is intrinsically perceived complexly with bottom-up and top-down strategies combined in human vision. To address this problem, this study proposes a novel end-toend trainable network comprising a backbone net and two branch nets. The backbone net uses ground-truth masks as top-down guidance for saliency prediction, whereas the two branch nets construct triplet proposals for regional feature mapping and clustering, which drives the network to be bottom-up sensitive to co-salient regions. We construct a new dataset of 2019 natural images with co-saliency in each image to evaluate the proposed method. Experimental results show that the proposed method achieves state-of-the-art accuracy with a running speed of 28 fps.

关键词： saliency detection convolutional neural network regional feature mapping co-saliency detection deep learning

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A blockchain-based privacy-preserving and collusion-resistant scheme(PPCR)for double auctions

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Digital Communications and Networks 2025年第1期11卷 116-125页

作者： Xuedan Jia Liangmin Wang Ke Cheng Pujie Jing Xiangmei Song School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang 212013China School of Cyber Science and Engineering Southeast UniversityNanjing 211102China School of Computer Science and Technology Xidian UniversityXi’an 710071China

Electronic auctions(e-auctions)remove the physical limitations of traditional auctions and bring this mechanism to the general ***,most e-auction schemes involve a trusted auctioneer,which is not always credible in *** studies have applied cryptography tools to solve this problem by distributing trust,but they ignore the existence of *** this paper,a blockchain-based Privacy-Preserving and Collusion-Resistant scheme(PPCR)for double auctions is proposed by employing both cryptography and blockchain technology,which is the first decentralized and collusion-resistant double auction scheme that guarantees bidder anonymity and bid privacy.A two-server-based auction framework is designed to support off-chain allocation with privacy preservation and on-chain dispute resolution for collusion resistance.A Dispute Resolution agreement(DR)is provided to the auctioneer to prove that they have conducted the auction correctly and the result is fair and *** addition,a Concise Dispute Resolution protocol(CDR)is designed to handle situations where the number of accused winners is small,significantly reducing the computation cost of dispute *** experimental results confirm that PPCR can indeed achieve efficient collusion resistance and verifiability of auction results with low on-chain and off-chain computational overhead.

关键词： Privacy protection Collusion resistance Secure protocol Blockchain-based double auction Dispute resolution

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CoDetect: cooperative anomaly detection with privacy protection towards UAV swarm

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science China(Information sciences) 2024年第5期67卷 325-326页

作者： Teng LI Weiguo LIN Ruichen MA Zhuo MA Yulong SHEN Jianfeng MA School of Cyber Engineering Xidian University School of Computer Science and Technology Xidian University

As mission scenarios grow more complex, the role of a single unmanned aerial vehicle(UAV) is limited. The UAV swarm,surpassing the single UAV, dynamically establishes temporary communication networks, ensuring high transmission rates and expansive communication coverage [1]. Unlike a single UAV, a swarm faces increased security risks, requiring specific capabilities.(1) Anomalies detection.

关键词： Unmanned aerial vehicles (UAV)

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LT-DBFT: A Hierarchical Blockchain Consensus Using Location and Trust in IoT

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IEEE Internet of Things Journal 2025年第12期12卷 19574-19589页

作者： Wang, Yang Xing, Xiaofei Li, Peiqiang Wang, Guojun Guangzhou University School of Computer Science and Cyber Engineering Guangzhou510006 China Guangzhou University School of Computer Science and Network Engineering Guangzhou510006 China

The exponential growth of IoT devices has led to the proposal of edge computing for data processing. The decentralized nature of edge computing servers and IoT devices makes blockchain ideal for connecting IoT users and servers. The consensus protocol, a core technology in blockchain, ensures node agreement and operational efficiency. However, as node numbers increase and spread geographically, traditional consensus protocols face deployment challenges, significantly reducing efficiency. To address this, we propose a hierarchical blockchain consensus protocol based on geographic location and a trust model called Location and Trust Delegated-BFT (LT-DBFT). This protocol reduces global communication delays caused by wide geographic distribution by assigning consensus nodes to different clusters based on their locations. A trust model is also designed to elect active nodes for consensus voting, thereby reducing the consensus overhead within clusters. Subsequently, the primary nodes of each cluster form a global shared layer to achieve the final ordering and execution of transactions. Through theoretical and experimental analysis, our scheme demonstrates lower latency and higher throughput performance than traditional PBFT and GeoBFT, making it more suitable for efficient deployment in large-scale and geographically widespread IoT environments. © 2014 IEEE.

关键词： Internet of Things Blockchains Security Protocols Consensus protocol Complexity theory Fault tolerant systems Fault tolerance Servers Scalability

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Formal Verification of Data Modifications in Cloud Block Storage Based on Separation Logic

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Chinese Journal of Electronics 2024年第1期33卷 112-127页

作者： Bowen ZHANG Zhao JIN Hanpin WANG Yongzhi CAO Key Laboratory of High Confidence Software Technologies (MOE) School of Computer Science Peking University School of Computer and Artificial Intelligence Zhengzhou University School of Computer Science and Cyber Engineering Guangzhou University

Cloud storage is now widely used, but its reliability has always been a major concern. Cloud block storage(CBS) is a famous type of cloud storage. It has the closest architecture to the underlying storage and can provide interfaces for other types. Data modifications in CBS have potential risks such as null reference or data *** verification of these operations can improve the reliability of CBS to some extent. Although separation logic is a mainstream approach to verifying program correctness, the complex architecture of CBS creates some challenges for verifications. This paper develops a proof system based on separation logic for verifying the CBS data modifications. The proof system can represent the CBS architecture, describe the properties of the CBS system state, and specify the behavior of CBS data modifications. Using the interactive verification approach from Coq, the proof system is implemented as a verification tool. With this tool, the paper builds machine-checked proofs for the functional correctness of CBS data modifications. This work can thus analyze the reliability of cloud storage from a formal perspective.

关键词： Cloud computing Behavioral sciences Reliability Formal verification

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A metadata-aware detection model for fake restaurant reviews based on multimodal fusion

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Neural Computing and Applications 2025年第1期37卷 475-498页

作者： Jian, Yifei Chen, Xinyu Wang, Xiaoda Liu, Ying Chen, Xingshu Lan, Xiao Wang, Wenxian Wang, Haizhou School of Cyber Science and Engineering Sichuan University Chengdu China College of Computer Science Sichuan University Chengdu China Cyber Science Research Institute Sichuan University Chengdu China

Nowadays, online reviews have a significant impact on customers who purchase products or evaluate services, as well as merchants who do business. Unfortunately, there are a lot of fake reviews included in them, which badly affect consumers’ decision-making. Fake review detection has been a significant focus of many researchers in the last few years. Existing approaches mostly focus on the review text, the behavior of reviewers, and the relationship between review entities, while ignoring the abundant metadata in reviews. In this research, we construct the first publicly available dataset of fake restaurant review detection with rich metadata. Through detailed analysis of our dataset, we extract 30 metadata features and text features to help improve the detection performance of models, including 10 brand-new features as well as 9 redefined features. Moreover, a novel metadata-aware model for fake restaurant review detection is proposed. In this model, review text is fed to a transformer model and a convolutional neural network based on pre-training strategies, and the extracted features are fed to a multi-layer perceptron with an attention mechanism. Such a structure enables an effective combination of text and rich metadata. Experimental results show that our proposed model has good stability and robustness and achieves an accuracy of 93.12%, outperforming the baselines by 1.73% in generalization ability test. The fake review dataset and innovative framework proposed in this study can provide ideas and solutions for future research. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Extracted features Fake review detection Metadata-aware model Rich metadata

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Energy consumption dynamic prediction for HVAC systems based on feature clustering deconstruction and model training adaptation

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Building Simulation 2024年第9期17卷 1439-1460页

作者： Huiheng Liu Yanchen Liu Huakun Huang Huijun Wu Yu Huang School of Civil Engineering and Transportation Guangzhou UniversityGuangzhou510006China School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510006China

The prediction of building energy consumption offers essential technical support for intelligent operation and maintenance of buildings,promoting energy conservation and low-carbon *** paper focused on the energy consumption of heating,ventilation and air conditioning(HVAC)systems operating under various modes across different *** constructed multi-attribute and high-dimensional clustering vectors that encompass indoor and outdoor environmental parameters,along with historical energy consumption *** enhance the K-means algorithm,we employed statistical feature extraction and dimensional normalization(SFEDN)to facilitate data clustering and *** method,combined with the gated recurrent unit(GRU)prediction model employing adaptive training based on the Particle Swarm Optimization algorithm,was evaluated for robustness and stability through k-fold *** the clustering-based modeling framework,optimal submodels were configured based on the statistical features of historical 24-hour data to achieve dynamic prediction using multiple *** dynamic prediction models with SFEDN cluster showed a 11.9%reduction in root mean square error(RMSE)compared to static prediction,achieving a coefficient of determination(R2)of 0.890 and a mean absolute percentage error(MAPE)reduction of 19.9%.When compared to dynamic prediction based on single-attribute of HVAC systems energy consumption clustering modeling,RMSE decreased by 12.6%,R2 increased by 4.0%,and MAPE decreased by 26.3%.The dynamic prediction performance demonstrated that the SFEDN clustering method surpasses conventional clustering method,and multi-attribute clustering modeling outperforms single-attribute modeling.

关键词： HVAC system energy consumption clustering analysis deep learning model adaptation dynamic prediction

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