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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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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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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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HM-QCNN: Hybrid Multi-branches Quantum-Classical Neural Network for Image Classification 9th

HM-QCNN: Hybrid Multi-branches Quantum-Classical Neural Netw...

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19th International Conference on advanced Data Mining and Applications, ADMA 2023

作者： Liu, Haowen Gao, Yufei Shi, Lei Wei, Lin Shan, Zheng Zhao, Bo School of Cyber Science and Engineering Songshan Laboratory Zhengzhou University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9783031466632

Quantum machine learning has been developing in recent years, demonstrating great potential in various research domains and promising applications for pattern recognition. However, due to the constraints of quantum hardware, the input qubits are restricted caused by small circuit size, and the fuzziness in all dimensions caused by the features that are difficult to be effectively mined. Besides, previous studies focus on binary classification, but multi-classification received little attention. To address the difficulty in multi-classification, this paper proposed a hybrid multi-branches quantum-classical neural network (HM-QCNN) that utilizes a multi-branch strategy to construct the convolutional part. The part consists of three branches to extract the features of different scales and morphologies. Two quantum convolutional layers apply quantum CRZ gates and rotational gates to design a random quantum circuit (RQC) with 4 qubits and full qubits measurements. The experiments on three public datasets (MNIST, Fashion MNIST, and MedMNIST) demonstrate that HM-QCNN outperforms other prevalent methods with accuracy, precision, and convergence speed. Compared with the classical CNN and the hybrid neural network without multi-branches, HM-QCNN reached 97.40% and improved the accuracy of classification by 6.45% and 1.36% on the MNIST dataset, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Qubits

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An Opposition-Based Learning Adaptive Chaotic Particle Swarm Optimization Algorithm

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Journal of Bionic engineering 2024年第6期21卷 3076-3097页

作者： Chongyang Jiao Kunjie Yu Qinglei Zhou State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Strategic Support Force Information Engineering UniversityZhengzhou450001China Henan Information Engineering School Zhengzhou Vocational College of Industrial SafetyZhengzhou450000China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou450001China School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou450001China

To solve the shortcomings of Particle Swarm Optimization(PSO)algorithm,local optimization and slow convergence,an Opposition-based Learning Adaptive Chaotic PSO(LCPSO)algorithm was *** chaotic elite opposition-based learning process was applied to initialize the entire population,which enhanced the quality of the initial individuals and the population diversity,made the initial individuals distribute in the better quality areas,and accelerated the search efficiency of the *** inertia weights were adaptively customized during evolution in the light of the degree of premature convergence to balance the local and global search abilities of the algorithm,and the reverse search strategy was introduced to increase the chances of the algorithm escaping the local *** LCPSO algorithm is contrasted to other intelligent algorithms on 10 benchmark test functions with different characteristics,and the simulation experiments display that the proposed algorithm is superior to other intelligence algorithms in the global search ability,search accuracy and convergence *** addition,the robustness and effectiveness of the proposed algorithm are also verified by the simulation results of engineering design problems.

关键词： PSO Opposition-based learning Chaotic motion Inertia weight Intelligent algorithm

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Disentangling Ensemble Models on Adversarial Generalization in Image Classification 5

Disentangling Ensemble Models on Adversarial Generalization ...

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5th International Conference on Electronic engineering and Informatics, EEI 2023

作者： Li, Chenwei Pan, Mengyuan Yang, Bo Zhang, Hengwei Henan Key Laboratory of Information Security State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Guizhou University School of Chemistry and Chemical Engineering Guiyang China

ISBN: (纸本)9798350327076

Convolutional neural networks are widely used in computer vision and image processing. However, when the original input is added with manually imperceptible perturbations, these deep network models mostly tend to output incorrect predictions. The vulnerability of these models poses great threat to intelligent applications, and these manually imperceptible perturbations are called adversarial examples. Current baseline methods have achieved considerable white-box attack success rate, but black-box rate remains to be improved. To boost the adversarial generalization, ensemble models method is introduced to the process of generating adversarial examples. This paper proposes multiple ensemble strategies with baseline attack methods based on existing ensemble strategy used by former methods. Experiment on ImageNet dataset empirically verifies the optimal ensemble strategy on boosting adversarial generalization. © 2023 IEEE.

关键词： Image classification

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Honeypot Detection Method Based on Anomalous Requests Response Differences 23

Honeypot Detection Method Based on Anomalous Requests Respon...

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6th International Conference on Electronics, Communications and Control engineering, ICECC 2023

作者： Chen, Xingyu Lu, Bin Sun, Rongbo Jiang, Mi State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University China Cyber Security College Information Engineering University China

ISBN: (纸本)9798400700002

Honeypot detection is a popular technology in the current cyber security, which can be used to check the disguise and protection level of deployed honeypots. To address the problem of low detection accuracy of existing honeypot detection techniques, this paper proposes a honeypot detection method based on the differences of anomalous requests' response. The method uses the anomalous request packet construction method designed in this paper to construct anomalous request packets, and sends the constructed anomalous request packets to the identity-known devices to collect the responses. Combined with the responses analysis method designed in this paper, the responses are analyzed in terms of similarity in both content and structure dimensions, which enables the evaluation of anomalous request packets in turn, and the selection of those that can consistently trigger a differential response from the honeypots to form a probing packets set based on anomaly. A deep learning model aiming at honeypot detection is designed using the responses of the identity-known devices in response to the probing packets set based on anomaly. The model and the responses of the nodes to detect to the probing packets set based on anomaly are used to detect honeypots. Experiment shows that the method is able to detect the nodes to detect with an accuracy of 96.4%. © 2023 ACM.

关键词： Deep learning

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Pixel+ and Pixel++: Compact and Efficient Forward-Secure Multi-Signatures for PoS Blockchain Consensus 33

Pixel+ and Pixel++: Compact and Efficient Forward-Secure Mul...

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33rd USENIX Security Symposium, USENIX Security 2024

作者： Wei, Jianghong Tian, Guohua Wang, Ding Guo, Fuchun Susilo, Willy Chen, Xiaofeng Xidian University China State Key Laboratory of Mathematical Engineering and Advanced Computing China College of Cyber Science Nankai University China School of Computing and Information Technology University of Wollongong Australia

ISBN: (纸本)9781939133441

Multi-signature schemes have attracted considerable attention in recent years due to their popular applications in PoS blockchains. However, the use of general multi-signature schemes poses a critical threat to the security of PoS blockchains once signing keys get corrupted. That is, after an adversary obtains enough signing keys, it can break the immutable nature of PoS blockchains by forking the chain and modifying the history from some point in the past. Forward-secure multi-signature (FS-MS) schemes can overcome this issue by periodically updating signing keys. The only FS-MS construction currently available is Drijvers et al's Pixel, which builds on pairing groups and only achieves forward security at the time period level. In this work, we present new FS-MS constructions that either are free from pairing or capture forward security at the individual message level (i.e., fine-grained forward security). Our first construction Pixel+ works for a maximum number of time periods T. Pixel+ signatures consist of only one group element, and can be verified using two exponentiations. It is the first FS-MS from RSA assumption, and has 3.5x and 22.8x faster signing and verification than Pixel, respectively. Our second FS-MS construction Pixel++ is a pairing-based one. It immediately revokes the signing key's capacity of re-signing the message after creating a signature on this message, rather than at the end of the current time period. Thus, it provides more practical forward security than Pixel. On the other hand, Pixel++ is almost as efficient as Pixel in terms of signing and verification. Both Pixel+ and Pixel++ allow for non-interactive aggregation of signatures from independent signers and are proven to be secure in the random oracle model. In addition, they also support the aggregation of public keys, significantly reducing the storage overhead on PoS blockchains. We demonstrate how to integrate Pixel+ and Pixel++ into PoS blockchains. As a proof-of-concept, we prov

关键词： Blockchain

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Multiuser Computation Offloading for Long-Term Sequential Tasks in Mobile Edge computing Environments

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Tsinghua Science and Technology 2023年第1期28卷 93-104页

作者： Huanhuan Xu Jingya Zhou Wenqi Wei Baolei Cheng School of Computer Science and Technology Soochow UniversitySuzhou 215006China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125China Provincial Key Laboratory for Computer Information Processing Technology Soochow UniversitySuzhou 215006China School of Computer Science Georgia Institute of TechnologyAtlantaGA 30332USA

Mobile edge computing has shown its potential in serving emerging latency-sensitive mobile applications in ultra-dense 5G networks via offloading computation workloads from the remote cloud data center to the nearby network ***,current computation offloading studies in the heterogeneous edge environment face multifaceted challenges:Dependencies among computational tasks,resource competition among multiple users,and diverse long-term *** applications typically consist of several functionalities,and one huge category of the applications can be viewed as a series of sequential *** this study,we first proposed a novel multiuser computation offloading framework for long-term sequential ***,we presented a comprehensive analysis of the task offloading process in the framework and formally defined the multiuser sequential task offloading ***,we decoupled the long-term offloading problem into multiple single time slot offloading problems and proposed a novel adaptive method to solve *** further showed the substantial performance advantage of our proposed method on the basis of extensive experiments.

关键词： mobile edge computing sequential tasks computation offloading dependency

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