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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Cui, Lanlan Wang, Yichuan Xiao, Renzhi Li, Miao Liu, Xiaoxue Hei, Xinhong School of Computer Science and Engineering XI'AN University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China School of Software Engineering Jiangxi University of Science and Technology Nanchang China School of Information Engineering Zhongnan University of Economics and Law Wuhan China

ISBN: (纸本)9783982674100

With the advancement of NAND flash technology, the increased storage density leads to intensified interference, which in turn raises the error rate during data retrieval. To ensure data reliability, low-density parity-check (LDPC) codes are extensively employed for error correction in NAND flash memory. Although LDPC soft decision decoding offers high error correction capability, it comes with a significant latency. Conversely, hard-decision decoding, although faster, lacks sufficient error correction strength. Consequently, flash memory typically initiates with hard-decision decoding and resorts to multiple soft decision decoding upon failure. To minimize decoding latency, this paper proposes a decoding mechanism based on the double hard decision, called DHD. This DHD scheme improves the Log-Likelihood Ratio (LLR) in the hard decision process. After the first hard decision fails, the read reference voltage (RRV) is adjusted to perform the second hard decision decoding. If the second hard decision also fails, soft decision decoding is then employed. Experimental results demonstrate that when the Raw Bit Error Rate (RBER) is 8.5 × 10-3, DHD reduces the Frame Error Rate (FER) by 86.4% compared to the traditional method. © 2025 EDAA.

关键词： Error correction

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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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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

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

ISBN: (纸本)9781665468190

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. © 2022 IEEE.

关键词： Embeddings

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FISHFUZZ: catch deeper bugs by throwing larger nets 23

FISHFUZZ: catch deeper bugs by throwing larger nets

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Proceedings of the 32nd USENIX Conference on security Symposium

作者： Han Zheng Jiayuan Zhang Yuhang Huang Zezhong Ren He Wang Chunjie Cao Yuqing Zhang Flavio Toffalini Mathias Payer National Computer Network Intrusion Protection Center University of Chinese Academy of Science and School of Computer and Communication Sciences EPFL National Computer Network Intrusion Protection Center University of Chinese Academy of Science and School of Computer and Communication Lanzhou University of Technology National Computer Network Intrusion Protection Center University of Chinese Academy of Science National Computer Network Intrusion Protection Center University of Chinese Academy of Science and Zhongguancun Laboratory School of Cyber Engineering Xidian University School of Cyberspace Security Hainan University National Computer Network Intrusion Protection Center University of Chinese Academy of Science and Zhongguancun Laboratory and School of Cyberspace Security Hainan University and School of Cyber Engineering Xidian University School of Computer and Communication Sciences EPFL

ISBN: (纸本)9781939133373

Fuzzers effectively explore programs to discover bugs. Greybox fuzzers mutate seed inputs and observe their execution. Whenever a seed reaches new behavior (e.g., new code or higher execution frequency), it is stored for further mutation. Greybox fuzzers directly measure exploration and, by repeating execution of the same targets with large amounts of mutated seeds, passively exploit any lingering bugs. Directed greybox fuzzers (DGFs) narrow the search to a few code locations but so far generalize distance to all targets into a single score and do not prioritize targets *** introduces an input prioritization strategy that builds on three concepts: (i) a novel multi-distance metric whose precision is independent of the number of targets, (ii) a dynamic target ranking to automatically discard exhausted targets, and (iii) a smart queue culling algorithm, based on hyperparameters, that alternates between exploration and exploitation. FISHFUZZ enables fuzzers to seamlessly scale among thousands of targets and prioritize seeds toward interesting locations, thus achieving more comprehensive program testing. To demonstrate generality, we implement FISHFUZZ over two well-established greybox fuzzers (AFL and AFL++). We evaluate FISHFUZZ by leveraging all sanitizer labels as targets. In comparison to modern DGFs and state-of-the-art coverage guided fuzzers, FISHFUZZ reaches higher coverage compared to the direct competitors, finds up to 2.8x more bugs compared with the baseline and reproduces 68.3% existing bugs faster. FISHFUZZ also discovers 56 new bugs (38 CVEs) in 47 programs.

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MICABAC: Multidimensional Industrial Control Attribute-Based Access Control Model

MICABAC: Multidimensional Industrial Control Attribute-Based...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Hangyu Wang Fei Lv Yuqi Chen Shuaizong Si Zhiwen Pan Degang Sun Limin Sun Beijing Key Laboratory of IOT Information Security Technology Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Shanghai Tech University Shanghai China Computer Network Information Center Chinese Academy of Sciences Beijing China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

As the Industrial Control System (ICS) increasingly merges with the Internet, the security threats have been increasing from internal users and external hackers. These challenges are further intensified by the facts: industrial control devices and protocols, leading to the inadequacy of traditional access control models in tackling the intricacies of ICS. We identify attributes that are optimally aligned with the specific needs of the ICS environment and propose the Multidimensional Industrial Control Attribute-Based Access Control Model (MICABAC) as a customized solution. MICABAC model significantly improves access control security and is finer granularity by selecting and evaluating required attributes within various ICS. We have been validated in two real-world ICS environments: the Gas Pipe network System (GPNS) and the computer Numerical Control (CNC) machine tool. Experiments indicate that by integrating MICABAC into the existing system, the maximum delay for access requests is 63.83 ms. In terms of accuracy in defending against malicious attacks, the GPNS achieves 96.49% and the CNC reaches 94.86%. Finally, we discuss the advantages and limitations of MICABAC and explore potential directions for future research.

关键词： Access control Protocols Industrial control Computational modeling Numerical models Machine tools Internet Integrated circuit modeling computer numerical control network systems

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Cyber Deception System based on Monte Carlo Simulation in the Mobile Edge Computing (MEC)

Cyber Deception System based on Monte Carlo Simulation in th...

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International Conference on computer, Information and Telecommunication Systems (CITS)

作者： Joelle Kabdjou Elie Fute Tagne Danda B. Rawat Jaime Acosta Charles Kamhoua Department of Mathematics and Computer Science University of Dschang Dschang Cameroon Department of Computer Engineering University of Buea Buea Dschang Cameroon Department of Electrical Engineering and Computer Science Howard University Washington USA DEVCOM Army Research Laboratory Network Security Branch Adelphi US

The frequency of malicious activities by cyber attackers is on the rise, posing a significant challenge to counter cyber attacks in the Mobile Edge Computing (MEC) environment. The vulnerability of wireless networks, which serve as the primary communication media for MEC servers and IoT applications, is due to their open nature of communication. This work proposes a MEC architecture that utilizes network Function Virtualization (NFV) and Service Function Chain (SFC) to combat cyber adversaries. Additionally, we evaluate the performance of a cyber deception system, which leverages a software-defined networking (SDN) controller and decision- maker to continuously monitor incoming MEC traffic and direct cyber adversaries to a deception MEC server. The deception MEC server collects information about cyber adversaries' intent, capabilities, and potential damage to the system, thus securing legitimate users from cyber attacks. The proposed approach was evaluated using Monte Carlo simulations, and numerous results were obtained which demonstrate the effectiveness of cyber deception in MEC.

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On the Effectiveness of Distillation in Mitigating Backdoors in Pre-trained Encoder

arXiv

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arXiv 2024年

作者： Han, Tingxu Huang, Shenghan Ding, Ziqi Sun, Weisong Feng, Yebo Fang, Chunrong Li, Jun Qian, Hanwei Wu, Cong Zhang, Quanjun Liu, Yang Chen, Zhenyu The State Key Laboratory for Novel Software Technology Nanjing University Jiangsu210093 China The School of Computer Science University of New South Wales NSW2052 Australia The School of Computer Science and Engineering Nanyang Technological University Nanyang639798 Singapore The Department of Computer Science The Director of the Network & Security Research Laboratory The University of Oregon United States

With the development of deep learning, self-supervised learning (SSL) has become particularly popular. SSL pre-trains an encoder (e.g., an image encoder) to learn generic features from a large amount of unlabeled data, releases it on a third-party platform, and allows any downstream task (e.g., an image classification task) to further fine-tune the encoder with task-specific data to derive the model for the task. However, despite the tremendous performance improvements it brings, SSL faces serious security risks. An adversary can poison pre-training datasets used to pre-train an encoder or publish a poisoned encoder directly to third-party platforms, which will also poison the model for any downstream task that uses these encoders. In this paper, we study a defense against poisoned encoders in SSL called distillation, which is a defense used in supervised learning originally. Distillation aims to distill knowledge from a given model (a.k.a the teacher net) and transfer it to another (a.k.a the student net). Now, we use it to distill benign knowledge from poisoned pre-trained encoders and transfer it to a new encoder, resulting in a clean pre-trained encoder. In particular, we conduct an empirical study on the effectiveness and performance of distillation against poisoned encoders. Using two state-of-the-art backdoor attacks against pre-trained image encoders and four commonly used image classification datasets, our experimental results show that distillation can reduce attack success rate from 80.87% to 27.51% while suffering a 6.35% loss in accuracy. Moreover, we investigate the impact of three core components of distillation on performance: Teacher net, student net, and distillation loss. By comparing 4 different teacher nets, 3 student nets, and 6 distillation losses, we find that fine-tuned teacher nets, warm-up-training-based student nets, and attention-based distillation loss perform best, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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Multi-Channel Hypergraph Convolution Group Recommendation with Member Information Enhancement 25

Multi-Channel Hypergraph Convolution Group Recommendation wi...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Chen, Tianhao Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

Group recommendation involves comprehensively considering various aspects, including members and items, to predict the overall interests of a group and recommend suitable items through a recommendation system. With the rapid evolution of the Internet, online group activities have become increasingly prevalent, making group recommendation a highly discussed topic within the realm of recommendation systems. However, current research in group recommendation still confronts the following challenges. Firstly, a predominant portion of group recommendation research focuses solely on aggregating group-level information, neglecting the valuable contribution of higher-order member-level insights to group recommendation. Moreover, some aggregation strategies overly prioritize fairness, thereby disregarding common real-world patterns. Secondly, previous studies have primarily relied on aggregating individual member interests to establish group preferences, lacking a holistic consideration of group dynamics. To tackle these challenges, this study introduces an innovative approach by implementing multi-channel hypergraph convolution within the member perspective. This approach aims to effectively extract higher-order insights from members by utilizing a member information enhancement module. By establishing group interconnections based on similarity and employing an adaptive fusion network to amalgamate multiple viewpoints, a final representation is derived. Experimental results demonstrate that our proposed model surpasses baseline models by 3% to 4% in terms of hit rate and accuracy, validating the efficacy of our approach. © 2023 IEEE.

关键词： Recommender systems

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Adaptive Compression-Encryption Scheme for Medical Image Based on Improved Compressive Sensing and Deoxyribonucleic Acid Coding-Compression

Adaptive Compression-Encryption Scheme for Medical Image Bas...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xianglian Xue Haiyan Jin Changjun Zhou School of Computer Science and Engineering Xi’an University of Technology Xi’an China Section of Computer Teaching and Research Shaanxi University of Chinese Medicine Xianyang China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China School of Computer Science and Technology Zhejiang Normal University Jinhua China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Medical images often occupy large storage space and contain patient privacy or sensitive information, which makes them difficult and unsafe to be transmitted through the network. This paper proposed an adaptive compression-encryption scheme based on improved compressive sensing (CS) and deoxyribonucleic acid (DNA) coding-compression, which helps to solve the above problem. In the proposed scheme, first the original medical image was compressed to a floating point CS matrix using the discrete wavelet transform and the partial Hadamard matrix. The CS matrix was then normalized and rounded for matrix quantification. The result of quantification was fed to DNA fixed encoding and DNA run length coding for encryption and secondary compression. Finally the compressed-encrypted image was obtained after DNA dynamic encoding-decoding and regroups operation. The proposed scheme was tested against 9 images and proved to be effective in reducing the quantization errors and enhancing the compression performance. For instance, when the benchmark compression ratio (CR) is 0.5, the CR can be reduced by 5%(CR = 0.4453) ∼ 23%(CR = 0.2636), with the corresponding peak signal-to-noise ratio values consistently surpassing the benchmark. Furthermore, the execution of DNA compression and DNA dynamic encoding-decoding provided double guarantee for the algorithm’s security.

关键词： Image coding Heuristic algorithms DNA Transforms Benchmark testing Encoding Sensors Compressed sensing Secure storage Biomedical imaging

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Social-enhanced recommendation using graph-based contrastive learning 25

Social-enhanced recommendation using graph-based contrastive...

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作者： Xue, Peng Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

The social network-based recommendation model use social network information to mitigate data sparsity issues and improve the accuracy of recommendation models. However, In most social network-based recommendation algorithms, the social neighbors' contributions are difficult to distinguish from the central user's, neglecting hidden correlations in social infor-mation. In order to solve this problem, this paper presents a graph-based contrastive learning framework for social-enhanced recommendation (SoGCLR) that utilizes implicit social information captured by a new social relation attention mechanism, en-rich user representations, and improve model robustness through graph-based contrastive learning. Specifically, the paper captures the degree of contribution of each neighbor in the social graph to the central user through a social relation attention layer, thus obtaining hidden correlations in social information, and further integrates this with user information in the user-item interaction graph to enrich user representations. In addition, the paper incorporates graph-based contrastive learning into the recommendation task using cross-layer contrastive learning, calculating contrastive loss and mapping nodes with similar but different exposure rates to nearby regions to mitigate exposure bias issues. Results from Ciao and Epinions demonstrate that SoGCLR reduces RMSE and MAE by 1.33% to 1.84% compared with baseline models. © 2023 IEEE.

关键词： Recommender systems

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Shellshock Bash Vulnerability Modeling Analysis Based on Petri Net

Shellshock Bash Vulnerability Modeling Analysis Based on Pet...

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2021 International Conference on networking and network Applications, NaNA 2021

作者： Zhang, Liumei Deng, Xi Wang, Yichuan Xi'an Shiyou University School of Computer Science Xi'an China Xi'an University of Technology School of Computer Science and Engineering Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technolog Xi'an China

ISBN: (纸本)9781665441582

The enhancement of Internet connectivity and the increase of information transmission speed yield the increasing frequency of network attacks. The Shellshock attackers often exploit bash vulnerability to read the content behind the function definition when importing environment variable functions. Then, malicious scripts can be executed in systems and servers, which compromises everything. Therefore, this paper proposes a formal modeling analysis method for the Shellshock Bash basis and automates the analysis of the patched position of the model. The relationship between the established model and the actual attack process is also discussed, which is a feasible reference for exploring unknown vulnerabilities and the location of corresponding patches. © 2021 IEEE.

关键词： Petri nets

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