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检索条件"机构=the State Key Laboratory of Mathematic Engineering and Advanced Computing"

共 1584 条记录，以下是11-20 订阅

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Adversarial Example Attacks Against Intelligent Malware Detection: A Survey 4

Adversarial Example Attacks Against Intelligent Malware Dete...

引用

4th International Conference on Applied Machine Learning, ICAML 2022

作者： Qi, Xuyan Tang, Yonghe Wang, Huanwei Liu, Tieming Jing, Jing State Key Laboratory of Mathematic Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665462655

With the advancement of information technology and the increasing prevalence of the internet, the number of malware has experienced an exponential, and the security threat to users' property and privacy is becoming more serious. With the great success of artificial intelligence especially deep learning technology in fields such as computer vision and speech processing, deep learning are gradually introduced into the field of malware detection, to process large amounts of malware more efficiently. However, in recent years, researchers have discovered that the deep learning model itself also has security risks. While malware detection based on deep learning brings convenience and technical progress, it also introduces some new threats, such as adversarial examples. This paper investigates adversarial example attacks against deep learning-based malware detectors. First, We reviewed research on malware detection techniques. Then we analyze the works related to adversarial example attacks against intelligent malware detectors. Finally, we discuss the challenges and prospects in future research. © 2022 IEEE.

关键词： Malware

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Meta Transductive Linear Probing for Few-Shot Node Classification 5

Meta Transductive Linear Probing for Few-Shot Node Classific...

引用

5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Lan, Mingjing Huang, Ningbo Zhou, Gang Xia, Yi State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9798350355925

Graph few-shot learning aims to predict well by training with very few labeled data. Meta learning has been the most popular solution for few-shot learning problem. However, transductive linear probing shows that fine-tuning a simple linear classification head after a pretrained graph neural networks can outperforms most of the sophisticated-designed graph meta learning algorithms. Therefore, in the paper, we propose a meta transductive linear probing methods named Meta-TLP to incorporate the advantages of graph self-supervised and graph meta learning model. Specifically, the graph neural network is firstly pretrained with graph contrastive learning methods. Then we design an unsupervised meta training task construction methods to require meta tasks without relying on labeled data. Finally, we meta training the linear classification head on the meta training tasks to learn to fast adopt to novel classes. Experiment results show that our model can perform better than TLP on three real world datasets. © 2024 IEEE.

关键词： Self-supervised learning

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RLGBG: A Reachable Landmark Grouping Based IP Geolocation Method for Unreachable Target 29th

RLGBG: A Reachable Landmark Grouping Based IP Geolocation Me...

引用

29th International Conference on Computational and Experimental engineering and Sciences, ICCES 2023

作者： Zu, Shuodi Liu, Chong Zhang, Fan Ding, Shichang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (纸本)9783031429866

IP geolocation technology can estimate or determine the geographical location of terminal device through IP address, which is an important basis for location-based network services. The existing IP geolocation methods based on network measurement cannot accurately obtain the network environment when the target does not respond to the detection message, so it is difficult to achieve high-precision geolocation. To solve this problem, a reachable landmark grouping based IP geolocation method for unreachable target (RLGBG) is proposed in this manuscript. RLGBG first detects landmark data extensively, filters, evaluates and retains reliable and accessible landmarks. Secondly, RLGBG counts the topological relationship and geographical distribution among the reachable landmarks, groups the landmarks according to the common router, and divides the landmarks into different groups after checking by geographical location. Then, the continuous IP segments are divided into IP blocks according to landmark groups, and the location range of each IP block is calculated by the landmarks within each group. Finally, RLGBG counts the IP blocks containing the target, merges those IP blocks that are geographically coincident and topologically related, and estimates the target’s location by calculating the landmark center within the IP blocks. The geolocation experiments based on four cities in China show that RLGBG can effectively estimate the location of unreachable IP targets, and its accuracy and coverage are higher than the existing typical method and IP location databases. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Location

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Quantum computing control system configuration optimizer with adaptive genetic algorithm 9

Quantum computing control system configuration optimizer wit...

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9th International Symposium on Advances in Electrical, Electronics, and Computer engineering, ISAEECE 2024

作者： Huang, Shucheng Wang, Junchao Pang, Jianmin Shan, Zheng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China

ISBN: (纸本)9781510683303

As quantum computing continues to advance, the complexity of control systems for superconducting qubits also increases. This paper presents a genetic algorithm (GA) approach for optimizing the control systems in quantum computing. Focusing on minimizing costs, maximizing resource utilization, and reducing device count, the study demonstrates the algorithm's effectiveness in configuring control systems efficiently. Experimental results from five quantum chips, ranging from 64 to 1024 bits, demonstrate significant improvements in cost efficiency and device management after 500 generations of optimization. The findings highlight the algorithm's adaptability to different quantum system scales and its potential in enhancing the scalability and efficiency of quantum computing architectures. © 2024 SPIE.

关键词： Quantum computers

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TS-GGNN: Combining Graph and Sequence Features for Vulnerability Detection in Source Code 5

TS-GGNN: Combining Graph and Sequence Features for Vulnerabi...

引用

5th International Conference on Communications, Information System and Computer engineering, CISCE 2023

作者： Zhou, Xin Pang, Jianmin Zhang, Chunyan Yue, Feng Wang, Junchao Liu, Guangming State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9798350326796

Software vulnerability detection is crucial for maintaining the security and stability of software systems. In this paper, we propose a novel neural network model called TS-GGNN to address the problem of vulnerability detection in source code slices. The TS-GGNN model effectively captures both local and global features of vulnerable code by fusing sequence features with graph features. To achieve this, we utilize graph structure and sequence structure learning approaches to comprehensively extract valuable information from the source code slices. Our experiments are conducted on the SARD dataset, which consists of 61,638 code samples annotated for the presence or absence of vulnerabilities. The results demonstrate that TS-GGNN has the best vulnerability detection performance, with an accuracy of 99.4%, a precision of 98.81%, and an F1 score as high as 99.4% thereby validating the effectiveness of the TS-GGNN model in capturing features relevant to software vulnerabilities. © 2023 IEEE.

关键词： Network coding

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Research on Unpredetermined Behavior Recognition Techniques Based on Network Attacks 5

Research on Unpredetermined Behavior Recognition Techniques ...

引用

5th International Conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2023

作者： Xiao, Yuxuan Fei, Jinlong Wang, Junyi State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9798400716485

With the increasing number of cyber-attack behaviors, it has caused great harm to the contemporary society. Currently, the cyber attack behavior recognition module is usually implemented by adopting a strategy based on rule-base matching, which often fails to identify the attack behaviors when faced with unpreset path attack behaviors of the actual attack process due to its reliance on predefined abnormal behavior patterns and attack labels. Therefore, this paper proposes an efficient and accurate unpreset behavior detection framework for cyber-attack behaviors, introduces machine learning techniques into the identification of unpreset attack behaviors, proposes an RF-Corr feature importance assessment method based on the Kendall correlation coefficient between features and the feature weight values, and designs an unpreset attack behavior identification method based on BiLSTM neural network, which improves the current effectiveness of identification and detection for unpresetted behavior of cyber attacks, and provides a solution for artificial intelligence detection of unpresetted behavior of cyber attacks. © 2023 ACM.

关键词： Crime

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A Malware Analysis Method Based on Behavioral Knowledge Graph

A Malware Analysis Method Based on Behavioral Knowledge Grap...

引用

2022 International Conference on Electronic Information engineering and Computer Science, EIECS 2022

作者： Xu, Lianqiu Zhang, Chunyan Tang, Ke State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781510663312

In recent years, the rapid development of information technology has continuously brought risks and challenges to network security. How to effectively detect and classify malicious codes and avoid serious damage to information systems has become a current research focus. However, the existing malicious code analysis methods have insufficient semantic information and poor feature readability, making it difficult to discover the potential relationship between malicious codes. In order to solve these problems, this paper applies knowledge graph technology to the field of malicious code analysis, constructs malicious code behavior knowledge graph based on dynamic analysis design, and uses the representation learning model TransH to conduct knowledge graph embedding representation research, so as to use KNN algorithm to classify malicious sample families. The experimental results show that the method proposed in this paper has more advantages in the classification of malicious code families than the comparison model. © 2023 SPIE.

关键词： Knowledge graph

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Dynamic Resampling Based Boosting Random Forest for Network Anomaly Traffic Detection 36th

Dynamic Resampling Based Boosting Random Forest for Network...

引用

36th International Conference on Industrial, engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2023

作者： Ren, Huajuan Wang, Ruimin Dong, Weiyu Li, Junhao Tang, Yonghe State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9783031368219

Network anomaly traffic detection is an important technique for detecting intrusion activities and maintaining cyberspace security. Random forest is widely used in network anomalous traffic detection due to its good detection performance. However, Random Forest suffers from the insufficient ability to handle difficult samples and poor performance in dealing with imbalanced network traffic distribution. To address these two problems, a boosting random forest for network anomaly detection, called BRF, is proposed. The proposed method embeds the random forest model into Boosting training mechanism to enhance its classification ability for difficult samples. In the iterations, Random Forest is provided with relatively balanced and diverse training sets by dynamic resampling to alleviate the traffic imbalance problem. The effectiveness of BRF is demonstrated by multi-classification experiments on the NSL-KDD and UNSW-NB15 datasets. Compared with some shallow machine learning, deep learning, and ensemble learning methods, BRF has advantages in accuracy and time efficiency, which is a promising method for network anomaly detection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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An Improved key Mismatch Attack on Kyber 7

An Improved Key Mismatch Attack on Kyber

引用

7th International Conference on Cryptography, Security and Privacy, CSP 2023

作者： Wang, Yaru Jiang, Haodong Ma, Zhi State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9798350323368

Recently, American National Institute of Standards and Technology (NIST) announced Kyber as the first KEM candidate to be standardized. The security of Kyber is based on the modular learning with errors (MLWE) problem, which achieves excellent efficiency and size. This work proposes an improved key mismatch on Kyber, which can reduce the number of queries required to recover the secret key. We first transform the problem of finding a certain parameter of ciphertexts into a quantum ordered search problem. Then we give the procedure of finding the value of a parameter in the ciphertexts by the quantum method. Finally, we instantiate this attack method on Kyber512, Kyber768 and Kyber1024. Compared with the existing attack algorithm, our improved attack reduces the number of queries for Kyber512, Kyber768 and Kyber1024 by 63%, 59% and 45%, respectively. © 2023 IEEE.

关键词： Security of data

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Critical Variable Guided Mutation for Directed Greybox Fuzzing

Critical Variable Guided Mutation for Directed Greybox Fuzzi...

引用

2023 IEEE International Conference on Control, Electronics and Computer Technology, ICCECT 2023

作者： He, Yubo Liu, Long The State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9798350399028

Directed greybox fuzzing directs fuzzers to specified code areas and has gained great achievements in 1-day vulnerability detection. However, existing directed graybox fuzzers fail to generate the crash sample even if they found a testcase reaching the target site. There are mainly two questions that affect the effectiveness of directed greybox fuzzing: basic block-level target is coarse enough for 1-day vulnerability detection and the fuzzers follow a specific rule to select operators regardless of the vulnerability itself. This paper points out that only a few vulnerability-related variables are related to the vulnerability triggering. Based on the vulnerability-related variables, this paper proposes critical variable guided mutation, a mutation scheduling method to enhance the crash reproduction capability of directed greybox fuzzing. We implemented a prototype MDGF based on the critical variable guided mutation and evaluate it on real world programs. Evaluation of MDGF on various real-world programs showed that MDGF found vulnerabilities faster than the mainstream directed greybox fuzzers. The experimental results showed that the speed of MDGF is 6.18 times faster than that of AFLGo and 1.40 times faster than Beacon, and MDGF can find 1.71x more bugs than AFLGo. © 2023 IEEE.

关键词： Cell proliferation

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