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International Conference on Software engineering (ICSE)

作者： Yueming Wu Deqing Zou Shihan Dou Wei Yang Duo Xu Hai Jin Huazhong University of Science and Technology China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering HUST Wuhan China Services Computing Technology and System Lab National Engineering Research Center for Big Data Technology and System HUST Wuhan China Fudan University China University of Texas at Dallas United States Cluster and Grid Computing Lab School of Computer Science and Technology HUST Wuhan China

ISBN: (纸本)9781665495899

Since deep learning (DL) can automatically learn features from source code, it has been widely used to detect source code vulnerability. To achieve scalable vulnerability scanning, some prior studies intend to process the source code directly by treating them as text. To achieve accurate vulnerability detection, other approaches consider distilling the program semantics into graph representations and using them to detect vulnerability. In practice, text-based techniques are scalable but not accurate due to the lack of program semantics. Graph-based methods are accurate but not scalable since graph analysis is typically time-consuming. In this paper, we aim to achieve both scalability and accuracy on scanning large-scale source code vulnerabilities. Inspired by existing DL-based image classification which has the ability to analyze millions of images accurately, we prefer to use these techniques to accomplish our purpose. Specifically, we propose a novel idea that can efficiently convert the source code of a function into an image while preserving the program details. We implement Vul-CNN and evaluate it on a dataset of 13,687 vulnerable functions and 26,970 non-vulnerable functions. Experimental results report that VulCNN can achieve better accuracy than eight state-of-the-art vul-nerability detectors (i.e., Checkmarx, FlawFinder, RATS, TokenCNN, VulDeePecker, SySeVR, VulDeeLocator, and Devign). As for scalability, VulCNN is about four times faster than VulDeePecker and SySeVR, about 15 times faster than VulDeeLocator, and about six times faster than Devign. Furthermore, we conduct a case study on more than 25 million lines of code and the result indicates that VulCNN can detect large-scale vulnerability. Through the scanning reports, we finally discover 73 vulnerabilities that are not reported in NVD.

关键词： Deep learning Codes Scalability Semantics Detectors Transforms Rats

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Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical Guarantees

arXiv

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arXiv 2022年

作者： Wang, Binghui Li, Youqi Zhou, Pan Department of Computer Science Illinois Institute of Technology United States School of Cyberspace Science and Technology School of Computer Science Beijing Institute of Technology China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China

Graph neural networks (GNNs) have achieved state-of-the-art performance in many graph-based tasks such as node classification and graph classification. However, many recent works have demonstrated that an attacker can mislead GNN models by slightly perturbing the graph structure. Existing attacks to GNNs are either under the less practical threat model where the attacker is assumed to access the GNN model parameters, or under the practical black-box threat model but consider perturbing node features that are shown to be not enough effective. In this paper, we aim to bridge this gap and consider black-box attacks to GNNs with structure perturbation as well as with theoretical guarantees. We propose to address this challenge through bandit techniques. Specifically, we formulate our attack as an online optimization with bandit feedback. This original problem is essentially NP-hard due to the fact that perturbing the graph structure is a binary optimization problem. We then propose an online attack based on bandit optimization which is_proven to be sublinear to the query number T, i.e., O(√NT3/4) where N is the number of nodes in the graph. Finally, we evaluate our proposed attack by conducting experiments over multiple datasets and GNN models. The experimental results on various citation graphs and image graphs show that our attack is both effective and efficient. Source code is available at https://***/Metaoblivion/Bandit_GNN_Attack. © 2022, CC BY.

关键词： Graph neural networks

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Wavelike tunneling of phonons dominates glassy thermal conductivity in crystalline Cs3Bi2I6Cl3

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Physical Review B 2023年第22期108卷 224302-224302页

作者： Jingyu Li Liuming Wei Zhuoyang Ti Le Ma Yuli Yan Guangbiao Zhang Peng-Fei Liu Institute for Computational Materials Science School of Physics and Electronics International Joint Research Laboratory of New Energy Materials and Devices of Henan Province Henan University Kaifeng 475004 China Institute of High Energy Physics Chinese Academy of Sciences Beijing 100049 China Spallation Neutron Source Science Center Dongguan 523803 China Department of Network Security Henan Police College Zhengzhou 450046 China Science Island Branch of Graduate School University of Science and Technology of China Hefei 230026 China School of Materials Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China

Intrinsically low lattice thermal conductivity κL in halide perovskites is of great interest for energy conversion applications. Here, based on first-principles calculations, we systematically study the lattice thermal conductivity of the recently synthesized layered perovskite Cs3Bi2I6Cl3. By using renormalized force constants extracted from lattice dynamics, our calculated κL is 0.227 and 0.130 Wm−1K−1 along the in-plane and cross-plane directions at 300 K, respectively, which agrees well with the experimental values (0.223 and 0.209 Wm−1K−1 parallel and perpendicular to the Bridgman growth direction). Meanwhile, κL follows a nonstandard κL∝T−0.237 dependence on heating, originating from the dual particle-wave behavior of heat-carrying phonons where wavelike tunneling dominates >72% of the contribution to the total κL when T> 300 K. Further analyses imply that Cs3Bi2I6Cl3 manifests the coexistence of metavalent bonding, loosely bonded rattling atoms with thermally induced large-amplitude vibrations, and stereochemical lone pair activity, which induces strong anharmonicity with the soft low-lying modes, causes a mixed crystalline-liquid state, and, finally, produces unexpectedly glassy thermal conductivity. Our work pinpoints the microscopic origin of ultralow κL in Cs3Bi2I6Cl3, which is important for designing efficient materials in halide perovskites for energy conversion.

关键词： Anharmonic lattice dynamics Phonons Thermal conductivity Perovskites Density functional theory

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Accurate Leukocyte Detection Based on Deformable-DETR and Multi-Level Feature Fusion for Aiding Diagnosis of Blood Diseases

arXiv

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arXiv 2024年

作者： Chen, Yifei Zhang, Chenyan Chen, Ben Huang, Yiyu Sun, Yifei Wang, Changmiao Fu, Xianjun Dai, Yuxing Qin, Feiwei Peng, Yong Gao, Yu HDU-ITMO Joint Institute Hangzhou Dianzi University Hangzhou310018 China Medical Big Data Lab Shenzhen Research Institute of Big Data Shenzhen518172 China School of Artificial Intelligence Zhejiang College of Security Technology Wenzhou325016 China National-local Joint Engineering Research Center for Digitalized Electrical Design Technology Wenzhou University Wenzhou325035 China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Department of Hematology Zhejiang Hospital Hangzhou310018 China

In standard hospital blood tests, the traditional process requires doctors to manually isolate leukocytes from microscopic images of patients’ blood using microscopes. These isolated leukocytes are then categorized via automatic leukocyte classifiers to determine the proportion and volume of different types of leukocytes present in the blood samples, aiding disease diagnosis. This methodology is not only time-consuming and labor-intensive, but it also has a high propensity for errors due to factors such as image quality and environmental conditions, which could potentially lead to incorrect subsequent classifications and misdiagnosis. Contemporary leukocyte detection methods exhibit limitations in dealing with images with fewer leukocyte features and the disparity in scale among different leukocytes, leading to unsatisfactory results in most instances. To address these issues, this paper proposes an innovative method of leukocyte detection: the Multi-level Feature Fusion and Deformable Self-attention DETR (MFDS-DETR). To tackle the issue of leukocyte scale disparity, we designed the High-level Screening-feature Fusion Pyramid (HS-FPN), enabling multi-level fusion. This model uses high-level features as weights to filter low-level feature information via a channel attention module and then merges the screened information with the high-level features, thus enhancing the model’s feature expression capability. Further, we address the issue of leukocyte feature scarcity by incorporating a multi-scale deformable self-attention module in the encoder and using the self-attention and cross-deformable attention mechanisms in the decoder, which aids in the extraction of the global features of the leukocyte feature maps. The effectiveness, superiority, and generalizability of the proposed MFDS-DETR method are confirmed through comparisons with other cutting-edge leukocyte detection models using the private WBCDD, public LISC and BCCD datasets. Our source code and private WBCCD

关键词： Object detection

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A Lightweight and Robust Access Control Protocol for IoT-based e-Healthcare Network

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IEEE Transactions on Mobile Computing 2025年

作者： Ghaffar, Zahid Kuo, Wen-Chung Mahmood, Khalid Tariq, Tayyaba Shamshad, Salman Das, Ashok Kumar Alenazi, Mohammed J.F. National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University Department of Computer Science and Engineering College of Informatics 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh Saudi Arabia

Internet of Things (IoT) devices are crucial components in e-healthcare networks. It enables remote patient health monitoring and facilitates seamless communication among medical sensors, wearable devices, and healthcare providers through public communication channels. Despite these advantages, the use of public communication among medical sensors in e-healthcare networks introduces critical challenges, such as vulnerability to impersonation, physical capture, and ephemeral secret leakage, particularly in resource-constrained environments. In recent years, various access control protocols have been developed to mitigate these risks. However, these protocols often fail to ensure robust security while incurring significant communication and computation overhead. To overcome these limitations, we propose a lightweight and robust access control protocol for IoT-based e-healthcare networks using chaotic maps. We propose a novel protocol that integrates a PUF-based mechanism to mitigate the challenges of physical tampering and cloning attacks in e-healthcare networks. It leverages the inherent uniqueness of PUF and enhances security through the high-entropy properties of chaotic maps. We analyze the proposed protocol informally, which confirms that it significantly bolsters efficiency and security. We also validate the security using the Random or Real (RoR) model. Moreover, we verify the security of the proposed protocol using Scyther. These analyses highlight that the proposed protocol offers robust resistance to numerous attacks, such as impersonation, physical capture, and ephemeral secret leakage. Moreover, we also compare it with existing and relevant protocols. The comparative analysis showcases its superior performance. Notably, the proposed authentication protocol significantly reduces 46.84% computational overhead and decreases 31.30% communication overhead, underscoring its enhanced performance and resource efficiency. © 2002-2012 IEEE.

关键词： Authentication

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Ecological security Evaluation Algorithm for Resource-Exhausted Cities Based on the PSR Model

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Computers, Materials & Continua 2021年第10期69卷 985-1001页

作者： Xiaozhou Li Zhenggang Xu Yuanyuan Fu Qi Jin Yunlin Zhao Neal NXiong Key Laboratory of Forestry Remote Sensing Based Big Data&Ecological Security for Hunan Province Central South University of Forestry and TechnologyChangsha410004China Hunan Urban and Rural Ecological Planning and Restoration Engineering Research Center Hunan City UniversityYiyang413000China College of Information and Electronic Engineering Hunan City UniversityYiyang413000China Department of Mathematics and Computer Science Northeastern State UniversityTahlequah74464USA

Today,resource depletion threatens a number of resource-based cities in *** ecological security problem caused by the long-term exploitation of natural resources is a key issue to be solved in the development of resource-exhausted *** 23 indicators,this study evaluated the ecological security status and development trends of 21 resource-exhausted cities in China from 2011 to *** results showed that from 2011 to 2015,the overall ecological security of this type of city was low,with over 60%of the cities at an unsafe ***,ecological security improved rapidly after 2016,and by 2017,all of the cities had reached the critical safety *** top 10 indicators of ecological security included industrial sulfur dioxide emissions,water supply,agricultural fertilizer application,and forest *** 10 indicators’cumulative contribution to ecological security was 48.3%;among them,reducing industrial sulfur dioxide emissions contributed the most at 5.7%.These findings can help governments better understand the ecological security status of resource-exhausted cities,and it can provide a reference for the allocation of funds and other resources to improve the ecological safety of these cities.

关键词： Ecological security PSR model evaluation resource-exhausted cities

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PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models Against Adversarial Examples

arXiv

引用

arXiv 2022年

作者： Hu, Shengshan Zhang, Junwei Liu, Wei Hou, Junhui Li, Minghui Zhang, Leo Yu Jin, Hai Sun, Lichao School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China Cluster and Grid Computing Lab China National Engineering Research Center for Big Data Technology and System Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China Services Computing Technology and System Lab China City University of Hong Kong Hong Kong Deakin University Australia Lehigh University United States

Point cloud completion, as the upstream procedure of 3D recognition and segmentation, has become an essential part of many tasks such as navigation and scene understanding. While various point cloud completion models have demonstrated their powerful capabilities, their robustness against adversarial attacks, which have been proven to be fatally malicious towards deep neural networks, remains unknown. In addition, existing attack approaches towards point cloud classifiers cannot be applied to the completion models due to different output forms and attack purposes. In order to evaluate the robustness of the completion models, we propose PointCA, the first adversarial attack against 3D point cloud completion models. PointCA can generate adversarial point clouds that maintain high similarity with the original ones, while being completed as another object with totally different semantic information. Specifically, we minimize the representation discrepancy between the adversarial example and the target point set to jointly explore the adversarial point clouds in the geometry space and the feature space. Furthermore, to launch a stealthier attack, we innovatively employ the neighbourhood density information to tailor the perturbation constraint, leading to geometry-aware and distribution-adaptive modification for each point. Extensive experiments against different premier point cloud completion networks show that PointCA can cause a performance degradation from 77.9% to 16.7%, with the structure chamfer distance kept below 0.01. We conclude that existing completion models are severely vulnerable to adversarial examples, and state-of-the-art defenses for point cloud classification will be partially invalid when applied to incomplete and uneven point cloud data. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Evaluating the Impact of Special Characters on Password security: a Comparative Analysis

引用

IEEE Network 2025年

作者： He, Daojing Liu, Zhiyong Chan, Sammy Zhou, Beibei Guizani, Mohsen Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Guizhou University State Key Laboratory of Public Big Data Guizhou China Research and Development Center Guangzhou Jingyuan Security Technology Co. Ltd Guangzhou China City University of Hong Kong Kowloon Department of Electrical Engineering Hong Kong Hong Kong Shanghai Urban Construction Vocational College School of Artificial Intelligence Application Shanghai China Machine Learning Department Abu Dhabi United Arab Emirates

Text-based password authentication remains essential for identity verification on internet platforms and blockchain systems. However, as computational power advances, security threats to user passwords have intensified, raising concerns about their effectiveness. While special characters (i.e., non-alphanumeric symbols, which include punctuation marks, mathematical symbols, and other non-letter/number characters like '.', '@', '#', '$', and similar) are often recommended to enhance password strength, the National Institute of Standards and technology (NIST) prioritizes length over complexity, challenging their necessity. This study fills a critical gap in understanding user behavior regarding special character usage. By analyzing 15 datasets from Chinese, English, and German languages, we examined patterns of special character incorporation and their impact on password strength. Our findings indicate that users frequently employ predictable methods for including special characters, potentially compromising security, especially under stringent policies. We present three key contributions: (1) an analysis of special character usage patterns, (2) a quantitative assessment of strength differences using the PCFGv4.1 model, and (3) actionable recommendations for stakeholders to enhance password security practices. This research further advocates for aligning password policies with user behavior for more effective security. © 1986-2012 IEEE.

关键词： Authentication Protocol

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FedPHE: A Secure and Efficient Federated Learning via Packed Homomorphic Encryption

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Li, Yuqing Yan, Nan Chen, Jing Wang, Xiong Hong, Jianan He, Kun Wang, Wei Li, Bo Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University RiZhao Information Technology Institute Rizhao276800 China Huazhong University of Science and Technology 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 Wuhan430074 China Shanghai Jiao Tong University School of Cyber Science and Engineering Shanghai200240 China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

Cross-silo federated learning (FL) enables multiple institutions (clients) to collaboratively build a global model without sharing private data. To prevent privacy leakage during aggregation, homomorphic encryption (HE) is widely used to encrypt model updates, yet incurs high computation and communication overheads. To reduce these overheads, packed HE (PHE) has been proposed to encrypt multiple plaintexts into a single ciphertext. However, the original design of PHE assumes all clients share a single private key, making the system vulnerable to security threats of ciphertexts being intercepted and decrypted by honest-but-curious clients. Also, it does not consider the heterogeneity among different clients, resulting in undermined training efficiency with slow convergence and stragglers. To address these challenges, we propose FedPHE, a secure and efficient FL framework with PHE by jointly exploiting contribution-aware secure aggregation and straggler-resistant client selection. Using CKKS with sparsification and blinding, FedPHE achieves efficient secure aggregation that allows clients to only provide obscured encrypted updates while the server can perform aggregation by accounting for contributions of local updates. To mitigate the straggler effect, we devise a perturbed sketch-based selection to cherry-pick representative clients with heterogeneous models and computing capabilities in a communication-efficient and privacy-preserving manner. We show, through rigorous security analysis and extensive experiments, that FedPHE can efficiently safeguard clients' privacy, achieve 2.45-6.56× training speedup, cut the communication overhead by 1.32-24.85×, and reduce straggler effects by 1.89-2.78×. © 2004-2012 IEEE.

关键词： Privacy by design

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Corrosion-resistant aluminum alloy design through machine learning combined with high-throughput calculations

arXiv

引用

arXiv 2023年

作者： Ji, Yucheng Fu, Xiaoqian Ding, Feng Xu, Yongtao He, Yang Ao, Min Xiao, Fulai Chen, Dihao Dey, Poulumi Xiao, Kui Ren, Jingli Li, Xiaogang Dong, Chaofang Ji, Y. Fu, X. Ding, F. He, Y. Ao, M. Chen, D. Xiao, K. Li, X. Dong, C. Ji, Y. Dey, P. Xu, Y. Xiao, F. Ren, J. Beijing Advanced Innovation Center for Materials Genome Engineering National Materials Corrosion Protection Data Center Institute for Advanced Materials and Technology Shunde Innovation School University of Science and Technology Beijing Beijing100083 China Department of Materials Science and Engineering Faculty of Mechanical Maritime and Materials Engineering Delft University of Technology Delft2628CD Netherlands National Engineering & Technology Research Center for Non-Ferrous Metals Composites GRINM Group Corporation Limited Beijing101407 China Shandong Nanshan Aluminum Co. Ltd. Longkou265706 China Henan Academy of Big Data Zhengzhou University Zhengzhou450001 China

Efficiently designing lightweight alloys with combined high corrosion resistance and mechanical properties remains an enduring topic in materials engineering. To this end, machine learning (ML) coupled ab-initio calculations is proposed within this study. Due to the inadequate accuracy of conventional stress-strain ML models caused by corrosion factors, a novel reinforcement self-learning ML algorithm (accuracy R2 >0.92) is developed. Then, a strategy that integrates ML models, calculated energetics and mechanical moduli is implemented to optimize the Al alloys. Next, this Computation Designed Corrosion-Resistant Al alloy is fabricated that verified the simulation. The performance (elongation reaches ~30%) is attributed to the H-captured Al-Sc-Cu phases (-1.44 eV H-1) and Cu-modified η/η' precipitation inside the grain boundaries (GBs). The developed Al-Mg-Zn-Cu interatomic potential (energy accuracy 6.50 meV atom-1) proves the cracking resistance of the GB region enhanced by Cu-modification. Conceptually, our strategy is of practical importance for designing new alloys exhibiting corrosion resistance and mechanical properties. Copyright © 2023, The Authors. All rights reserved.

关键词： Aluminum alloys

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