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Polymorphic signal responses of phase-change quantum dot string upon stochastic resonance

Nano Research 2025年第2期18卷 717-724页

作者： Qin Wan Fei Zeng Ziao Lu Junwei Yu Feng Pan Key Laboratory of Advanced Materials(MOE) School of Materials Science and EngineeringTsinghua UniversityBeijing 100084China Center for Brain Inspired Computing Research(CBICR) Tsinghua UniversityBeijing 100084China

We successfully constructed phase-change quantum dots string(PCQDS)systems and studied their signal *** PCQDs actually is a cascaded structure consisted of several stochastic resonance(SR)two-state systems,in which inherent non-linearity,i.e.,phase-change of quantum dots(QDs),plays elementary and important roles to modulate signal *** established an SR model to simulate signal responses depending on stimulation *** know that some QDs will oscillate with input forcing frequency,while certain QDs will oscillate in their own frequency triggered by phase *** two effectscooperate togeneratepolymorphic response patterns,including action potential patterns exhibited by envelope of spike peak *** interesting and important simulation is that we replicate the memory effect in Nb-doped AINO,i.e.,a QDs dispersed *** result indicates that memory can occur in a system only constructed by volatile elementary units,implying memory existing in ***-term plasticity and spike-rate dependent plasticity can also be realized by using frequency and phase *** study provides a new scope to study signal handling and memory effect in quantum system.

关键词： quantum-dot string phase-change inherent nonlinearity signal modulation niobium oxide stochastic resonance

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A Survey of Available Information Recovery of Binary Programs Based on Machine Learning 5

A Survey of Available Information Recovery of Binary Program...

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5th IEEE International Conference on Artificial Intelligence and Big Data, ICAIBD 2022

作者： Shao, Wenqiang Yang, Qichao Guo, Xixi Cai, Ruijie State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665499132

A binary program is an executable file obtained by compiling the source code. In order to reduce the size of software or prevent reverse engineering, software publishers usually only release executable files that have stripped a lot of source information. The rich source-level information is very helpful for software hardening and malware analysis. In recent years, the rapid development of artificial intelligence has promoted the research of binary program information recovery. This paper summarizes the relevant research on effective information recovery of binary programs since 2016. Firstly, the importance of recovering source-level information from binary programs is introduced. Next, the problems are divided into three categories, compiler traceability, signature information recovery and variable information recovery of source program. Finally, based on the latest technology in this field, the future development direction is analyzed. © 2022 IEEE.

关键词： Machine learning

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RSM-PA:Research on Route Selection Method of Inter-domain Routing System Based on Path Availability 23

RSM-PA:Research on Route Selection Method of Inter-domain Ro...

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23rd IEEE International Conference on High Performance computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Zhu, Huihu Qiu, Han Zhu, Junhu Wang, Qingxian State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665494571

The inter-domain routing system is the key infrastructure of the Internet. However, BGP does not consider the link state in the routing selection process. As a result, when the inter-domain routing system faces security incidents such as disasters and attacks, it is easy to cause the continuous propagation of link failures, resulting in a large-scale network disconnection and posing a considerable threat to the security of the Internet. The previous research mainly aims to enhance the protocol's safety, cannot change the original failure propagation mechanism, and cannot fundamentally solve cascading failure propagation. In response to this problem, this paper introduces SDN and proposes RSM-PA. This method perceives the available path topology by sharing the link state information in the region, provides a verification mechanism for the availability of the route in the routing process to avoid the link failure in the region due to overload. The comparison of routing results before and after the link state changes under the same experimental conditions shows that RSM-PA can effectively adjust the routing strategy according to the path availability, and lay a solid foundation for suppressing the propagation of cascading failures. © 2021 IEEE.

关键词： Network topology Heuristic algorithms Power system protection Routing Real-time systems Routing protocols Internet

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Yara rule enhancement using Bert-based strings language model 5

Yara rule enhancement using Bert-based strings language mode...

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5th International Conference on advanced Electronic Materials, Computers and Software engineering, AEMCSE 2022

作者： Xu, Lianqiu Qiao, Meng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781665484749

ISBN: (纸本)9781665484749

In recent years, with the development and popularity of information technology, how to protect information systems from malicious code is now a hot issue. Malicious code will cause irreversible damage through a series of malicious behaviors for information theft or system damage, so it needs to be detected before it has a bad impact. Yara is a common tool used by network security practitioners or analysts to effectively detect malicious code. However, Yara rules are generally written manually, and their quality is related to the level of analysts, which makes it difficult to guarantee the effectiveness of Yara detection. Some of the current work can automatically generate Yara rules, but most of the methods are blacklist matching in nature, which may cause a degradation of the detection effect when the database is small. To solve the above problems, this paper proposes the Bert-based strings language model (beslm), which can effectively learn the semantic information of Yara rules and the association relationship between strings through pre-training phase and finetuning phase, and can filter out high-quality strings and thus improve the detection effect. The experimental results demonstrate that the Yara rules automatically generated by beslm improve 13.3%, 13.8%, and 15.1% on average in three detection metrics compared with the comparison method. © 2022 IEEE.

关键词： Network security

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LOM: A Website Fingerprinting Defense Method based on Local Optimal Mutation 10

LOM: A Website Fingerprinting Defense Method based on Local ...

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10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022

作者： Wang, Jue Zhu, Yuefei Lin, Wei Li, Ding Tang, Ke State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

Website Fingerprinting (WF) is a traffic analysis attack. The potential local adversary can infer the user's web activity by extracting implicit pattern information from traffic traces. Leveraging the latest advances in Deep Learning, the adversary has achieved 98% detection accuracy in closed-world tests. This poses a new challenge to the traditional WF defense methods. In this paper, we propose a new defense method, the proposed dissimilarity measure and local optimal mutation strategy greatly reduce the computational cost. Experimental results show that the method achieves the generation of adversarial traffic trace with shorter preparation times and better defensive effects. Moreover, it also has great potential in the face of more powerful attacks that may emerge in the future. © 2022 IEEE.

关键词： Websites

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A Research and Verification of MFC Framework-aware Virus Infection Technology 3

A Research and Verification of MFC Framework-aware Virus Inf...

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3rd International Conference on Computer Vision, Image and Deep Learning and International Conference on Computer engineering and Applications, CVIDL and ICCEA 2022

作者： Guo, Wang Li, Yang Sha, Zihan Bu, Wenjuan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665459112

Portable Executable (PE) virus technology is an important threat factor in the field of network security. With continuous upgrading of malware detection technology, virus infection technology will also gradually evolve from infecting the structure of the PE file to infecting the deeper structure of the program, and thus achieve higher level of stealthiness and confrontation. This paper proposes a structure-aware virus infection technology for Microsoft Foundation Classes (MFC) software framework, aiming to explore a new method of virus infection. This research aims to verify the virus infection method of MFC program framework perception through experiments, and then provides a solution to the new PE virus, which can help improve the current level of heuristic malware detection technology. © 2022 IEEE.

关键词： Viruses

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Stance Detection for Social Text: Inference-Enhanced Multi-Task Learning with Machine-Annotated Supervision

Stance Detection for Social Text: Inference-Enhanced Multi-T...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Pi, Qiankun Lu, Jicang Sun, Yepeng Fan, Qinlong Zhou, Xukun Shang, Shouxin State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Zhengzhou China School of Information Renmin University of China Beijing China

ISBN: (纸本)9798350368741

Stance detection, a natural language processing technique, captures user attitudes on controversial social media topics. However, the semantic ambiguity of social texts makes accurate stance determination challenging. Existing annotated data is often domain-specific, resulting in poor model generalization for unseen targets and cross-domain scenarios. To tackle this challenge, we introduce inference tasks related to stance detection as auxiliary tasks and use a multitask learning approach to improve the model's understanding of textual semantics. Meanwhile, to alleviate the scarcity of annotated data, we use argumentation corpus with abundant resources as the source domain to train the basic stance detection model. We integrate this model with a large-scale framework to facilitate weakly supervised learning for machine annotation of unlabeled social texts, thereby boosting the model's adaptability across different targets and domains. Experimental results on four Twitter datasets demonstrate that our method can significantly improve the model's stance discriminative ability and generalization performance. © 2025 IEEE.

关键词： argumentation corpus machine annotation multitask learning stance detection

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UEFI Security Threats Introduced by S3 and Mitigation Measure 7

UEFI Security Threats Introduced by S3 and Mitigation Measur...

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7th International Conference on Signal and Image Processing, ICSIP 2022

作者： Jiao, Weihua Li, Qingbao Chen, Zhifeng Cao, Fei State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

UEFI is widely used as the new generation BIOS, except for some new features, it also brings some security issues. The threat brought by S3 sleep jeopardizes the security of computer platforms seriously. An attacker can disable the write protection of UEFI by tampering with the S3 boot script or execute arbitrary code by exploiting dispatch opcodes with the entry point out of SMRAM. Therefore, it is vital to solve the security issue related to S3 and propose effective mitigation measures. Based on the S3 related modules and the risk propagation between modules, we propose the MBPE algorithm to identify the key vulnerability of the platform through formal analysis. Aiming at this critical vulnerability, based on the LockBox protection mechanism, we propose a mitigation measure. It includes changing the storage time of the boot script, the changed storage address of the boot script and a specific UEFI variable. After taking this mitigation measure, the attacker cannot obtain the boot script and its location simultaneously. Such, it can protect the boot script from being tampered with and further attackers. Finally, based on the real attack cases, we evaluate the mitigation measure by the abstraction of attack path and mathematical methods. Based on the evaluation finding, the mitigation measures can prevent S3related attacks and effectively improve platform security. © 2022 IEEE.

关键词： Risk assessment

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VulD-CodeBERT: CodeBERT-Based Vulnerability Detection Model for C/C++ Code 6

VulD-CodeBERT: CodeBERT-Based Vulnerability Detection Model ...

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6th International Conference on Communications, Information System and Computer engineering, CISCE 2024

作者： Xiong, Zhe Dong, Weiyu School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou China Information Engineering University State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9798350353174

Recently, deep learning techniques have received widespread attention in code vulnerability and vulnerability detection. However, there are some problems with current vulnerability detection techniques, firstly, because of the non-homogeneity of vulnerability categories and quantities, there are omission false positives and overfitting problems for vulnerabilities with a small amount of data. Second, the existing detection methods have limited ability to characterize the code, there is insufficient depth of characterization, does not contain all of its vulnerability characteristics or is interspersed with useless information. Finally, the current research does not use the powerful feature mining capability of pre-trained models and does not employ model fusion to increase the effectiveness of model vulnerability detection. To solve the above problems, this paper proposes a new deep learning based pre-trained model for code vulnerability detection tool VulD-CodeBERT. By fine-tuning CodeBERT, a code adaptation extension for pre-trained models developed by Microsoft, the CodeBERT model can solve the problem of not being able to learn the vulnerability patterns due to insufficient data, thus resulting in missed false alarms. The sequence output from the CodeBERT model are added to a BiLSTM network with an attention mechanism to mine the local key features of the vulnerabilities combined with the global features of the pooler output from the CodeBERT model to deeply mine the dependencies between the vulnerability statements and derive the probability of the existence of the vulnerability. For vulnerability types that are difficult to uncover captured features or the number of samples is not enough, a polynomial plain Bayesian classifier is used for detection, and finally the two models are combined to deeply mine the vulnerability features, thus solving the problem of difficult and small samples, and reducing the leakage rate and false alarm rate. Experimental results sh

关键词： C++ (programming language)

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SPAFuzz: Web Security Fuzz Testing Tool Based on Fuzz Testing and Genetic Algorithm 4

SPAFuzz: Web Security Fuzz Testing Tool Based on Fuzz Testin...

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4th International Conference on Consumer Electronics and Computer engineering, ICCECE 2024

作者： Lin, Jiang Dong, Weiyu School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou China Information Engineering University State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9798350384062

Fuzzing is one of the most effective methods currently available for detecting web vulnerabilities. However, traditional fuzzing suffers from issues such as low coverage and limited diversity in test cases. To address these issues, we propose a way to generate test cases based on dynamic feature libraries and attack models by analysing the characteristics of SQL and XSS. Additionally, we introduce a genetic algorithm to guide the population's evolution by monitoring the state changes of the web application. Furthermore, we improved the selection phase of the genetic algorithm to optimally deform multiple iterations for use cases with low fitness values in order to preserve the population diversity to expand the attack capability of the test cases and based on this the SPAFuzz fuzzy testing tool was implemented. Experimental results show that SPAFuzz is able to detect more network vulnerabilities than Sqlmap and Powerfuzzer. © 2024 IEEE.

关键词： Genetic algorithms

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