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Precise and Efficient:A Hybrid Analysis-Based Stack Overflow Detection Model under MIPS

Precise and Efficient:A Hybrid Analysis-Based Stack Overflow...

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Smart World Congress (SWC), IEEE

作者： Yichuan Wang Chenhao Huang Tong Zhang Li Zhao Zhendong Gu Gangyang Li Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Hanjiang.to.Weihan River Valley Water Diversion Project Construction.co.ltd Xi’an China

Although the vulnerability detection techniques under the MIPS platform can detect stack overflow vulnerabilities, the stack overflow vulnerability of the stack frame changes is complex and hard to recover. The input generated by fuzzy testing techniques is inefficient and difficult to recover the site after generating crashes. It is a challenge to preserve the context of runtime stack and establish effective constraints under hybrid analysis techniques, which usually makes the solution path more run inefficient and affects the analysis results in accuracy. This paper proposes a method that combines hybrid analysis including taint analysis to detect the vulnerabilities of stack overflow. The method ensures accuracy while reducing the number of execution paths. The three vulnerabilities of stack overflow in the routed firmware selected for testing are successfully detected and the number of detected paths is controlled to about 10. In addition, the method is designed to decouple the vulnerability location and detection modules, providing a basis for subsequent research on a common vulnerability detection platform.

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Feature Selection Method Based on FSA-Choquet Fuzzy Integral

Feature Selection Method Based on FSA-Choquet Fuzzy Integral

引用

Industrial IoT, Big Data and Supply Chain (IIoTBDSC), International Conference on

作者： Zhangyu Xu Huaijun Wang Ruijie Wang Junhuai Li Xunchao Shang 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

With the instant development and wide application of sensor technology, human behavior recognition based on wearable sensors has attracted wide attention and become a hot research topic. The typical Choquet fuzzy integral feature selection method considers the interaction between classes, but the preferred features are not tested in the feature selection process. The selected feature subset is not suitable for different classifiers and different classification actions, and there are redundant features. Consequently, this paper proposes an adaptive feature selection method named Feature Selection based on the Adaptive Choquet fuzzy integral (FSA-Choquet). This proposed method can get the optimal subset by two times of feature selections. First, Choquet fuzzy integral is applied to optimize the current optimal feature subset. Then the feature selection is processed by combining the maximum redundancy calculation with the backward floating search strategy and classifier. At last, Experiments show that the feature based on FSA-Choquet selection has a higher classification recognition rate in behavior recognition. Under the comparison based on SVM classifier, the recognition accuracy of the features selected by FSA-Choquet is as high as 89.6 %, and the recognition rate of the features selected by Choquet is 77.36 %. Besides, Under the classifier based on Naive Bayes (NB), the classification recognition rate of FSA-Choquet is 91.67 %, while that of Choquet is only 82.3 %.

关键词： Support vector machines Redundancy Supply chains Diversity reception Big Data Feature extraction Search problems

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Unsupervised Anomalous Sound Detection for Mechanical Equipment Based on Domain Generalization Techniques

Unsupervised Anomalous Sound Detection for Mechanical Equipm...

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International Conference on Computer Supported Cooperative Work in Design

作者： Shumei Yang Kun Wang Kun Ma Fuqiang Wang Peng 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Qingdao Branch of China E-Port Data Center Qingdao China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Monitoring mechanical equipment is crucial for industrial efficiency and safety. Anomalous sound detection faces challenges due to limited labeled data and varying acoustic conditions. Supervised methods require extensive labeled data, which is often scarce. Unsupervised methods, which model the distributions of normal data, are more suitable when normal data is abundant but anomalies are rare. In this paper, we present SAM-AE-GAN, an unsupervised anomaly detection model that combines self-attention, autoencoders, GANs, and spectral perturbation for data augmentation. The self-attention mechanism captures long-range dependencies, autoencoders enable efficient data representation, and GANs enhance generative performance. Spectral perturbation enhances adaptability, improving robustness and accuracy. Experimental results demonstrate the effectiveness of SAM-AE-GAN across five machine types in the 2022 Challenge TASK 2 dataset, validating its capability in complex industrial environments. This model provides an effective solution for anomaly detection and predictive maintenance, even in scenarios with limited labeled data.

关键词： Adaptation models Accuracy Perturbation methods Autoencoders Data augmentation Generative adversarial networks Data models Robustness Anomaly detection Unsupervised learning

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DHD: Double Hard Decision Decoding Scheme for NAND Flash Memory

DHD: Double Hard Decision Decoding Scheme for NAND Flash Mem...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Lanlan Cui Yichuan Wang Renzhi Xiao Miao Li Xiaoxue Liu Xinhong Hei 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: (数字)9783982674100

ISBN: (纸本)9798331534646

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 $8.5 \times 10^{-3}$ , DHD reduces the Frame Error Rate (FER) by 86.4% compared to the traditional method.

关键词： Lead acid batteries Europe Voltage Interference Decoding Error correction Error correction codes Reliability Flash memories Iterative decoding

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Joint Entity and Relation Extraction Based on Prompt Learning and Multi-channel Heterogeneous Graph Enhancement

Joint Entity and Relation Extraction Based on Prompt Learnin...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Haiqing Lv Xiaohui Han Peipei Wang Wenbo Zuo Jing Guo Lijuan Xu 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Quan Cheng Laboratory Jinan China Information Research Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Joint extraction of entity and relation is crucial in information extraction, aiming to extract all relation triples from unstructured text. However, current joint extraction methods face two main issues. Firstly, they rarely consider the semantic information of entity and relation labels, leading to models that fail to fully understand and utilize the rich semantics in these labels, thereby limiting their performance. Secondly, although table-filling methods are widely used, they focus only on the start or end positions and ignore deep interactions between tables, relying solely on word-level information. To address these issues, we propose the P-MHE framework based on prompt learning and multi-channel heterogeneous graph enhancement. First, we use prompt templates to construct semantic nodes for entity and relation type labels, initializing them along with words as nodes in a heterogeneous graph. We iteratively fuse these semantic nodes through a message-passing mechanism to obtain node representations suitable for entity and relation extraction tasks. Secondly, we design a multi-channel heterogeneous graph to model node relationships from different perspectives, enhancing feature interactions among different types of nodes. Finally, we aggregate the semantic node information of entity and relation type labels after iteration, constructing separate decoding tables for each entity and relation type to better adapt to their respective characteristics. We evaluated our model on four public datasets. Experimental results show that P-MHE outperforms existing models on multiple public datasets. Extensive additional experiments further validate the effectiveness of our model.

关键词： Training Adaptation models Limiting Fuses Semantics Natural languages Information retrieval Feature extraction Data mining Iterative decoding

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A security Enhancement Scheme For Raft Consensus Algorithm Against Term Forgery Attacks

A Security Enhancement Scheme For Raft Consensus Algorithm A...

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Yichuan Wang Mengjie Tian Yaling Zhang Xiaoxue Liu Yeqiu Xiao Xinhong Hei 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

The consensus algorithm is the core component of blockchain technology and a key link to maintaining the security of the blockchain network. In the Raft consensus algorithm, since the term growth range is not specified in the algorithm, malicious nodes can obtain the leader status by increasing their term value, and the fairness of the Raft cluster leader election is damaged. To solve the above problems, in this paper, we restore the attack experiment, which is a malicious node gaining leader status by increasing its term value. Identify malicious nodes by dynamically adjusting the growth range of the term, thus achieving a defensive effect. The optimized algorithm resists malicious node attacks with a 79.4% probability and reduces the election time by 11.4% compared to the Raft consensus algorithm in the presence of malicious nodes. On the premise of ensuring high efficiency, the fault tolerance and security of the algorithm have also been significantly improved.

关键词： Fault tolerance Voting Fault tolerant systems Clustering algorithms Consensus algorithm Resists Ubiquitous computing

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Pervasive Container computing–––a Fine-Grained Loosely Coupled Cloud Cluster

Pervasive Container Computing–––a Fine-Grained Loosely Co...

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Xinhong Hei Dan Wu YiFan Liu Wenjiang Ji Yichuan Wang Lei Zhu 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

With the emergence of CaaS, a cloud service model that can quickly build a production environment, this model has been widely used in the industry. However, the degree of loose coupling of various containers in the cluster and the fine-grained level of function division are often difficult to achieve people’s expectations. In addition, it is difficult to achieve the desired effect in terms of cloud cluster security. Therefore, this paper takes container-based cloud computing clusters as the main research object, and proposes a fine-grained, loosely coupled cloud cluster model based on containers, is called Pervasive-container computing. The experimental results show that Pervasive-container computing not only meets the fine-grained loose coupling characteristics required by microservices, but also adopts the strategy of “created with time” and “destroy after use”, which provides guarantees in terms of endogenous security. In addition, the proposed scheduling algorithm based on Pervasive-container calculation reduces the time consumption of file transfer by 20% compared to the time consumption without the algorithm.

关键词： Couplings Industries Cloud computing Scheduling algorithms Computational modeling Microservice architectures Production

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Cross-chain Jamming Attack with Light Client Verification Clash in IBC Protocol

Cross-chain Jamming Attack with Light Client Verification Cl...

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Yaling Zhang Ziyan Wang Yichuan Wang Xiaoxue Liu Xinhong Hei 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

Inter-Blockchain Communication (IBC) in Cosmos is a Sidechains/Relays cross-chain protocol. During the implementation of the protocol, the blockchain uses relayers to establish connections, and uses light clients to track and verify the consensus state of counterparties. However, light clients lack effective malicious behavior detection mechanisms, which easily induces the effective attacks to cross-chain communication. This paper proposes a light client jamming attack in IBC cross-chain protocol. Attackers force the victim’s light client to be in the verification state frequently by continuously generating headers containing a large number of verifier signatures. By interfering with light clients, attackers can reduce the victim’s cross-chain transaction throughput with low cost and increase the probability of cross chain transaction failure. Based on the Cosmos blockchain, this paper tests the attack, models the attack process and analyzes its security. The experiment shows that when the header constructed by the attacker contains $5.0\times 10^{4}$ signatures, the probability of successful attack can reach 90%. Finally, this paper briefly introduces some feasible defense strategies against this attack.

关键词： Analytical models Protocols Costs Force Formal languages Throughput Ubiquitous computing

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An Efficient Energy Consumption Prediction Framework for High Performance computing Cluster Jobs

An Efficient Energy Consumption Prediction Framework for Hig...

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International Conference on Computer Engineering and Applications (ICCEA)

作者： Yantao Lou Jibin Wang Shoupeng Feng Xian Yu 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 Department of Public Security of Shandong Province Shandong Intelligent Public Security Technology Innovation Center Jinan China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

Energy consumption prediction is a crucial approach to enhance the operational efficiency of HPC (High Performance computing) clusters. Existing predicting methods for the energy consumption mainly rely on time series mode, focusing on the instantaneous power during job execution. However, in practical applications, due to the large scale of HPC clusters, the prediction models need to process a vast amount of data, often resulting in poor performance. Moreover, it's challenging to provide effective predictions for jobs with significant power fluctuations. Thus we propose a more efficient energy consumption prediction framework for HPC cluster jobs, achieving two main objectives: On one hand, it uses a data encoding module to pre-encode the sampled data of energy consumption related indicators, simplifying the data that the time series prediction model needs to process and improving prediction efficiency. On the other hand, the prediction target is set to the power distribution range in a future period of time, rather than the instantaneous power at some specific time points, which improves the usability of the prediction results. Test results show that the proposed prediction framework can achieve more markable performance improvement while ensuring the effectiveness of the predictions.

关键词： Energy consumption Computational modeling High performance computing Time series analysis Power distribution Predictive models Feature extraction

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EML-SAM: Incorporating Multi-Scale Fusion in SAM for Cine-CMR Segmentation

EML-SAM: Incorporating Multi-Scale Fusion in SAM for Cine-CM...

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International Conference on Computer Supported Cooperative Work in Design

作者： Huijuan Hao Wenpeng Wang Qingyan Ding Haibo Cheng Fengqi Hao Hu Zhang Jinqiang Bai 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Faculty of Data Science City University of Macau Macau China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Accurate and reproducible assessment of myocardial conditions is essential for the diagnosis of prior infarctions, cardiomyopathies, and inflammatory diseases. Although cardiac magnetic resonance imaging (CMR) is regarded as the gold standard for evaluating myocardial anatomy and function, manual segmentation remains labor-intensive and susceptible to variability. The demand for high-resolution images, dense predictions, and comprehensive segmentation across all phases of the cardiac cycle introduces significant challenges for Cine-CMR analysis. To address these issues, we present EML-SAM, an innovative interactive segmentation model based on the Segment Anything Model (SAM). EML-SAM incorporates an early-mid-late (EML) multi-scale fusion strategy, effectively mitigating the premature dilution of interaction information, thereby reducing segmentation errors and enhancing feature utilization. Furthermore, the model utilizes a multi-scale linear attention (MLA) transformer to establish an efficient global receptive field. Comprising 12 Transformer layers, the model integrates multi-scale attention with multi-scale perceptrons, striking a balance between computational efficiency and capacity. The proposed methodology offers a robust and efficient solution for high-resolution Cine-CMR segmentation, delivering accurate and real-time segmentation performance throughout the various stages of temporal changes within the cardiac cycle.

关键词： Image segmentation Accuracy Computational modeling Refining Myocardium Transformers Real-time systems Reproducibility of results Reliability Diseases

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