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International Joint Conference on Neural networks (IJCNN)

作者： Xu Xu Chong Fu Junxin Chen Gwanggil Jeon David Camacho School of Computer Science and Engineering Northeastern University Shenyang China School of Software Dalian University of Technology Dalian China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China College of Information Technology Incheon National University Incheon Korea Department of Computer Systems Engineering Universidad Politécnica de Madrid Madrid Spain

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Imagined speech production with electrocorticography (ECoG) plays a crucial role in brain-computer interface system. A challenging issue is the great variation underlying the frequency bands of the ECoG signals’ encode information, which makes current methods difficult to generate imagined speech with stable quality among different persons. To this end, we propose a robust model to generate high-quality imagined speech from ECoG. A frequency enhancement branch is first designed to adaptively modulate the frequency information, whose product is fed into the following multi-scale channel attention module for robust feature extraction and fusion. By incorporating both the mel-spectrum and audio as training constraints, a multi-constraint decoder branch is finally constructed for imagined speech production. The performance of our model is evaluated on a high-quality dateset, i.e, Single Word Production Dutch-iBIDS. It yields Pearson correlation scores that are all above 0.8, and the standard deviationsare are all below 0.2 in different volunteers. Experimental results demonstrate that our model is effective and robust for ECoG based imagined speech production, and has advantages over peer methods.

关键词： Training Adaptation models Frequency modulation Correlation Neuroscience Production Speech enhancement

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G²Face: High-Fidelity Reversible Face Anonymization via Generative and Geometric Priors

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IEEE Transactions on Information Forensics and security 2024年 19卷 8773-8785页

作者： Haoxin Yang Xuemiao Xu Cheng Xu Huaidong Zhang Jing Qin Yi Wang Pheng-Ann Heng Shengfeng He School of Computer Science and Engineering South China University of Technology Guangzhou China Guangdong Engineering Center for Large Model and GenAI Technology State Key Laboratory of Subtropical Building and Urban Science Guangzhou China Centre for Smart Health The Hong Kong Polytechnic University Hong Kong China School of Future Technology South China University of Technology Guangzhou China School of Computer Science and Network Security Dongguan University of Technology Dongguan China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong SAR China School of Computing and Information Systems Singapore Management University Victoria St Singapore

Reversible face anonymization, unlike traditional face pixelization, seeks to replace sensitive identity information in facial images with synthesized alternatives, preserving privacy without sacrificing image clarity. Traditional methods, such as encoder-decoder networks, often result in significant loss of facial details due to their limited learning capacity. Additionally, relying on latent manipulation in pre-trained GANs can lead to changes in ID-irrelevant attributes, adversely affecting data utility due to GAN inversion inaccuracies. This paper introduces G2Face, which leverages both generative and geometric priors to enhance identity manipulation, achieving high-quality reversible face anonymization without compromising data utility. We utilize a 3D face model to extract geometric information from the input face, integrating it with a pre-trained GAN-based decoder. This synergy of generative and geometric priors allows the decoder to produce realistic anonymized faces with consistent geometry. Moreover, multi-scale facial features are extracted from the original face and combined with the decoder using our novel identity-aware feature fusion blocks (IFF). This integration enables precise blending of the generated facial patterns with the original ID-irrelevant features, resulting in accurate identity manipulation. Extensive experiments demonstrate that our method outperforms existing state-of-the-art techniques in face anonymization and recovery, while preserving high data utility. Code is available at https://***/Harxis/G2Face .

关键词： Faces Data privacy Information integrity Information filtering Face recognition Generative adversarial networks Feature extraction

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GrayINT – Detection and Localization of Gray Failures via Hybrid In-band network Telemetry

GrayINT – Detection and Localization of Gray Failures via H...

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Asia-Pacific network Operations and Management Symposium (APNOMS)

作者： Kuichao Zhang Wei Su Huiling Shi Kai Zhang Wei Zhang 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 National Engineering Laboratory for Next Generation Internet Interconnection Devices (NGIID) School of Electronics and Information Engineering Beijing Jiaotong University Beijing China

As the underlying infrastructure, data center networks (DCNs) provide an important platform for cloud computing and next-generation networks. However, due to the influence of many factors, such as network, hardware, and software, various failures may occur during the operation of the DCN, which brings great challenges to its reliability. Particularly, a more insidious type of network failure, known as gray failure, presents a significant threat to system stability. In response to this challenge, in this work, we propose GrayINT, a gray failure detection and localization system that leverages In-band network Telemetry (INT) technology to monitor all paths within the data center network in real-time. To verify the feasibility of our design, we build a fat-tree data center network on a virtual network testbed. A large number of experimental results demonstrate that our system can monitor device and link status in real-time, achieving gray failure detection and localization.

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A DoS-attack Resilient Control Approach for AIOT-enabled Systems

A DoS-attack Resilient Control Approach for AIOT-enabled Sys...

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Artificial Intelligence of Things and Systems (AIoTSys), International Conference on

作者： Xiaoya Cao Zhenya Chen Weisheng Liu Yuanlong Liu Wenting Wang Xin Wang Ming Yang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center 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 State Grid Shandong Electric Power Company Jinan China State Grid Shandong Electric Power Research Institute Jinan China

This paper presents a novel controller design for dealing with the challenges posed by Denial-of-Service (DoS) attacks in the context of Artificial Intelligence of Things (AIOT). The proposed design employs a prediction approach that utilizes uniform quantization for both encoder and decoder, aiming to effectively mitigate data loss during data transmission in scenarios involving DoS attacks. Furthermore, the study investigates the application of dynamic system design as a means to enhance the security defense mechanisms of AIOT, consequently bolstering the overall resilience of the system. The effectiveness and practicality of the proposed approach are validated through comprehensive simulations, providing empirical evidence of its efficacy.

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Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

Prediction of exosomal piRNAs based on deep learning for seq...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Yulian Ding Aimin Li Rong Fei Xie Guo Fangxiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Computer Science Department of Mechanical Engineering Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs’ functions.

关键词： Proteins Location awareness RNA Biological system modeling Predictive models Feature extraction Natural language processing

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Dynamic Detection of Malicious Code on Android Based on Improved Multi-feature Gaussian Kernel 15th

Dynamic Detection of Malicious Code on Android Based on Impr...

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15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th International Conference on Harmony Search, Soft computing and Applications, ICHSA 2019

作者： Yu, Qing Luo, Xixi Wang, Zuohua Tianjin Key Laboratory of Intelligence Computing and Network Security Tianjin University of Technology Tianjin300384 China China Everbright Bank Co. Ltd. Tianjin300384 China

ISBN: (纸本)9783030325909

In recent years, with the increasing demand for mobile Internet, smartphones represented by the Android system begin to play an increasingly important role in people’s daily life. Due to the payment and social functions of smartphones, users’ property safety and personal privacy are increasingly threatened by malicious mobile apps. Lured by great interests, malicious applications began to spread in the mobile platform, malicious code is escalating, Android-side security is facing great threats. Therefore, it is very important to study the detection of malicious code in Android. This paper studies and analyzes the status of Android malicious code detection. On the basis of dynamic detection, this paper presents a dynamic detection algorithm of multi-feature IGK. The algorithm optimizes feature selection and Gaussian kernel function and applies the improved algorithm to multi-feature training to improve the detection accuracy of malicious code. We track application system calls and system service calls while the system is running, extract features from system call sequences and system service calls. Then we use the improved weighted information gain method to select the features. Finally, in view of the deficiencies of the traditional Gaussian kernel function in recognition rate and processing time, an improved Gaussian kernel function is proposed for machine learning. We evaluated our approach with 800 benign applications and 1200 malicious applications. The experimental results show that by the above two improvements, the highest detection rate is 97.5%, better than the existing methods. © 2020, Springer Nature Switzerland AG.

关键词： Smartphones

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Cetus: an efficient symmetric searchable encryption against file-injection attack with SGX

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Science China(Information Sciences) 2021年第8期64卷 195-212页

作者： Yanyu HUANG Siyi LV Zheli LIU Xiangfu SONG Jin LI Yali YUAN Changyu Dong College of Cyber Science and the College of Computer Science Tianjin Key Laboratory of Network and Data Security TechnologyNankai University School of Computer Science and Technology Shandong University School of Computer Science Guangzhou University Department of Computer Science University of G?ttingen School of Computing Newcastle University

Symmetric searchable encryption(SSE) allows the users to store and query their private data in the encrypted database. Many SSE schemes for different scenarios have been proposed in the past few years, however, most of these schemes still face more or fewer security issues. Using these security leakages,many attacks against the SSE scheme have been proposed, and especially the non-adaptive file injection attack is the most serious. Non-adaptive file injection attack(NAFA) can effectively recover some extremely important private information such as keyword plaintext. As of now, there is no scheme that can effectively defend against such attacks. We first propose the new security attribute called toward privacy to resist nonadaptive file injection attacks. We then present an efficient SSE construction called Cetus to achieve toward privacy. By setting up a buffer and designing the efficient oblivious reading algorithm based on software guard extensions(SGX), we propose the efficient one-time oblivious writing mechanism. Oblivious writing protects the update pattern and allows search operations to be performed directly on the data. The experiment results show that Cetus achieves O(aw) search time and O(1) update communication. The practical search time, communication, and computation overheads incurred by Cetus are lower than those of state-of-the-art.

关键词： searchable encryption SGX technique file injection attack forward/toward privacy cloud databases

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Social Recommendation Combining Implicit Information and Rating Bias

Social Recommendation Combining Implicit Information and Rat...

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International Conference on Computer Supported Cooperative Work in Design

作者： Weizhi Ying Qing Yu Zuohua Wang Tianjin Key Laboratory of Intelligence Computing and Network Security Tianjin University of Technology Tianjin China China Everbright Bank CO.LTD Beijing China

ISBN: (纸本)9781728165974;9781728165981

In recent years, more and more recommendation algorithm considers social information. However, in the existing social recommendation algorithm focused on the rating prediction task, the user's rating bias and the item's rating bias are often not considered. Besides, the existing social recommendation algorithms usually fail to deal with the sparse problem of social information. Concerning the problem of ignores rating bias and social information sparsity in the social recommendation, this paper proposes a novel recommendation model that combines implicit information and rating bias based on the traditional matrix factorization model. The social information used includes explicit social information and implicit social information generated by collaborative user network embedding methods. Experimental results on two real public datasets show that compared with other recommendation models, the proposed model reduces 1.13% to 10.09% and 3.14% to 8.38% respectively in the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). It shows that our proposed recommendation algorithm has a better recommendation effect.

关键词： Computational modeling Conferences Collaboration Transforms Prediction algorithms Collaborative work Sparse matrices

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AsyncSC: An Asynchronous Sidechain for Multi-Domain Data Exchange in Internet of Things

arXiv

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arXiv 2024年

作者： Yang, Lingxiao Dong, Xuewen Wan, Zhiguo Gao, Sheng Tong, Wei Lu, Di Shen, Yulong Du, Xiaojiang The School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'An710071 China The Zhejiang Lab Hangzhou311121 China The School of Information Central University of Finance and Economics Beijing100081 China The School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou310018 China The School of Computer Science and Technology Xidian University China The Shaanxi Key Laboratory of Network and System Security Xi'An710071 China The School of Engineering and Science Stevens Institute of Technology Hoboken07030 United States

Sidechain techniques improve blockchain scalability and interoperability, providing decentralized exchange and cross-chain collaboration solutions for Internet of Things (IoT) data across various domains. However, current state-of-the-art (SOTA) schemes for IoT multi-domain data exchange are constrained by the need for synchronous networks, hindering efficient cross-chain interactions in discontinuous networks and leading to suboptimal data exchange. In this paper, we propose AsyncSC, a novel asynchronous sidechain construction. It employs a committee to provide Cross-Blockchain as a Service (C-BaaS) for data exchange in multi-domain IoT. To fulfill the need for asynchronous and efficient data exchange, we combine the ideas of aggregate signatures and verifiable delay functions to devise a novel cryptographic primitive called delayed aggregate signature (DAS), which constructs asynchronous cross-chain proofs (ACPs) that ensure the security of cross-chain interactions. To ensure the consistency of asynchronous transactions, we propose a multilevel buffered transaction pool that guarantees the transaction sequencing. We analyze and prove the security of AsyncSC, simulate an asynchronous communication environment, and conduct a comprehensive evaluation. The results show that AsyncSC outperforms SOTA schemes, improving throughput by an average of 1.21 to 3.96 times, reducing transaction latency by 59.76% to 83.61%, and maintaining comparable resource overhead. Copyright © 2024, The Authors. All rights reserved.

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A Comprehensive Survey on Edge Data Integrity Verification: Fundamentals and Future Trends

arXiv

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arXiv 2022年

作者： Zhao, Yao Qu, Youyang Xiang, Yong Uddin, Md Palash Peng, Dezhong Gao, Longxiang School of Information Technology Deakin University Australia Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science China College of Computer Science Sichuan University China

Recent advances in edge computing (EC) have pushed cloud-based data caching services to edge, however, such emerging edge storage comes with numerous challenging and unique security issues. One of them is the problem of edge data integrity verification (EDIV) which coordinates multiple participants (e.g., data owners and edge nodes) to inspect whether data cached on edge is authentic. To date, various solutions have been proposed to address the EDIV problem, while there is no systematic review. Thus, we offer a comprehensive survey for the first time, aiming to show current research status, open problems, and potentially promising insights for readers to further investigate this under-explored field. Specifically, we begin by stating the significance of the EDIV problem, the integrity verification difference between data cached on cloud and edge, and three typical system models with corresponding inspection processes. To thoroughly assess prior research efforts, we synthesize a universal criteria framework that an effective verification approach should satisfy. On top of it, a schematic development timeline is developed to reveal the research advance on EDIV in a sequential manner, followed by a detailed review of the existing EDIV solutions. Finally, we highlight intriguing research challenges and possible directions for future work, along with a discussion on how forthcoming technology, e.g., machine learning and context-aware security, can augment security in EC. Given our findings, some major observations are: there is a noticeable trend to equip EDIV solutions with various functions and diversify study scenarios;completing EDIV within two types of participants (i.e., data owner and edge nodes) is garnering escalating interest among researchers;although the majority of existing methods rely on cryptography, emerging technology is being explored to handle the EDIV problem. Copyright © 2022, The Authors. All rights reserved.

关键词： Data integrity

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