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检索条件"机构=State Key Lab of Mathematical Engineering and Advanced Computing"

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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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An Efficient Sieve Algorithm with Evolutionary Technique 20

An Efficient Sieve Algorithm with Evolutionary Technique

引用

20th IEEE International Conference on Communication Technology, ICCT 2020

作者： Sun, Zedong Gu, Chunxiang Zheng, Yonghui State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781728181417

Sieve algorithm plays an important role in solving the shortest vector problem, which is one of the most representative hard lattice problems. In this paper, we propose an efficient sieve algorithm with evolutionary techniques. By combining Gauss Sieve algorithm with global selection, we propose an efficient sieve algorithm. Experimental results show that our algorithm performs better than FPLLL's Gauss Sieve algorithm in solving the shortest vector problem. Furthermore, we explore how mutation behaves in accelerating sieve algorithm. © 2020 IEEE.

关键词： Evolutionary algorithms

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An Ensemble Approach with Clustering-Based Under-sampling for Imbalanced Classification 2

An Ensemble Approach with Clustering-Based Under-sampling fo...

引用

2nd International Conference on Artificial Intelligence and Education, ICAIE 2021

作者： Ren, Huajuan Ren, Shuai Yan, Lin Wang, Ruimin Jing, Jing Shi, Jiaqi State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665424929

Class imbalance widely occurs in many real-world applications, which affects the recognition of important class to a certain extent. Ensemble methods that combined resampling are effective to alleviate the class imbalance problems. This paper presents a new ensemble approach with clustering-based under-sampling, called CNBoost, for learning from imbalanced data. This algorithm is based on the combination of Centers NN and boosting procedure. Centers NN, as an under-sampling utilizing the nearest neighbors of cluster centers, is used to provide a new training subset in each iteration of boosting, which makes the base learner learn the overall data distribution in each iteration of boosting. We compared the performance of the proposed algorithm with 3 popular ensemble methods. Out of 10 datasets and 3 measurements, CNBoost performs equally well or better than the other 3 methods in 25/30 categories. In addition, we discussed the effect of the base learner used in boosting on the performance of these algorithms. The results show that CNBoost is a promising approach with high classification accuracy and stability for dealing with imbalanced datasets. © 2021 IEEE.

关键词： Iterative methods

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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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A short text spectrum clustering method based on frequent itemsets 4

A short text spectrum clustering method based on frequent it...

引用

4th IEEE International Conference on Computer and Communications, ICCC 2018

作者： Luo, Nan Zhang, Ping Li, Qingbao Chen, Zhifeng Feng, Peijun Xue, Tianxiao Engineering and Advanced Computing State Key Laboratory of Mathematical Zhengzhou China

ISBN: (纸本)9781538683392

Short text datum, which contains a lot of useful information, could be easily found on a wide variety of self-media platform and social communication tools. on It is significant to use datum mining technology to automatically acquire knowledge from these datum. However, in short text datum, the word count is small, the text length is short and the information contained is little. Due to all these disadvantages, there are problems of feature sparseness and high information dimensionality. The accuracy of general text mining algorithms is usually unacceptable. This paper proposes a spectral clustering short text analysis method based on frequent itemsets. Firstly, the method cuts the short text datum, and compresses the datum using frequent itemsets. Secondly, a spectral algorithm based on Nyström sampling method is applied to perform clustering and topic extraction on the frequent itemsets. The method effectively solves the problems of feature sparseness and wide dimensions of short text datum. Experiments show that the methodhas better accuracy and performance than the existing algorithms when clustering short texts, and has a good clustering effect. © 2018 IEEE.

关键词： Data mining

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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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A PV-DM-based feature fusion method for binary malware clustering 4

A PV-DM-based feature fusion method for binary malware clust...

引用

4th International Conference on Communications, Information System and Computer engineering, CISCE 2022

作者： Gui, Hairen Zhang, Chunyan Huang, Yizhao Liu, Fudong State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781665498487

ISBN: (纸本)9781665498487

Utilizing NLP methods in malware classification or clustering applications is a hotspot in malware analysis. Binary malware embedding has become an important direction and basis for malware analysis. Existing binary malware vectorizing methods cannot extract semantic and functional information simultaneously, which may affect the performance of family clustering. In this paper, a novel feature fusion representation method is proposed based on PV-DM (Paragraph Vector-Distributed Memory) model, and the K-Means algorithm is adopted for family clustering. The method combines the features of program-level API call sequence and function-level instruction sequences by means of PV-DM. The functional and semantic representation capabilities of binary malwares can be improved. Malware public dataset VXHeavon is taken as the dataset. A series of experiments are conducted, using the existing family labels as the clustering accuracy evaluation standard. The results demonstrate that our feature fusion representation method outperforms the baseline method by 4.96% in terms of accuracy. © 2022 IEEE.

关键词： Malware

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Pdf Exploitable malware analysis based on exploit genes 12

Pdf Exploitable malware analysis based on exploit genes

引用

12th IEEE International Conference on Anti-Counterfeiting, Security, and Identification, ASID 2018

作者： Zhou, Xin Pang, Jianmin Liu, Fudong Wang, Jun Yue, Feng Liu, Xiaonan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781538660638

With the popularization of social networks, as a low-cost, high-efficiency entail attack method, most of the attack vectors were embedded in email attachments, and exploited vulnerability on Adobe and Office software. Among which PDF-based exploit samples are the main ones. In this paper, we combine bioinformatics and genetics and propose the pdf exploitable malware gene to analyze whether the exploits are exploited in the pdf malware based on software genes. We construct the experiments on the dataset collected from Virus Total filtered by the labels of multiple antivirus software. With the evaluation experiments, the results demonstrate the effectiveness of the pdf exploitable malware gene to detection and classification. © 2018 IEEE.

关键词： Genes

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A BOW-Based Sentence Embedding Method for Chinese Event Identification 4

A BOW-Based Sentence Embedding Method for Chinese Event Iden...

引用

2019 4th International Seminar on Computer Technology, Mechanical and Electrical engineering, ISCME 2019

作者： Gao, Lizheng Zhou, Gang Luo, Junyong State Key of Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Event extraction is an important branch of information extraction. It involves two challenging issues: event identification and argument identification. Most approaches of event identification are trigger-based, which suffer a lot from sample imbalance, word ambiguity, low scalability, etc. To solve these problems, we attempt to explore sentence-based event identification, whose main issue is sentence representation. With the success of word embedding, it has attracted much attention to generate semantic embeddings of sentences. In this paper, we propose a simple BOW-based sentence embedding method to represent event sentences for Chinese event identification. We compute embeddings on the dependency tree and map different relations to simple arithmetic operations. We evaluate our method with a dataset of Chinese event identification and compare the result with other BOW-based methods. The results show that our approach significantly outperforms other BOW-based methods. © Published under licence by IOP Publishing Ltd.

关键词： Embeddings

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TCLD-Net: A Transfer Learning Internet Encrypted Traffic Classification Scheme Based on Convolution Neural Network and Long Short-Term Memory Network

TCLD-Net: A Transfer Learning Internet Encrypted Traffic Cla...

引用

2021 IEEE International Conference on Communications, computing, Cybersecurity and Informatics, CCCI 2021

作者： Hu, Xinyi Gu, Chunxiang Chen, Yihang Wei, Fushan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665432085

The Internet is about to enter the era of full encryption. Traditional traffic classification methods only work well in non-encrypted environments. How to identify the specific types of network encrypted traffic in an encrypted environment without decryption is one of the foundations for maintaining cyberspace security. Traffic classification based on machine learning relies heavily on the prior knowledge of experts to construct feature sets. Although traffic classification based on deep learning can reduce human intervention, it requires a large amount of labeled data for parameter determination. This paper proposes a tCLD-Net model that combines transfer learning and deep learning. It can be trained on a small amount of labeled data to distinguish network encrypted traffic with a high accuracy. It pre-trains a CLD-Net model in the source domain data set, and fixes the parameters of the convolutional neural network module in it, and trains and tests it in the target domain data set. In order to verify the effectiveness of the tCLD-Net model, we use the ISCX public data set to conduct experiments. The results show that our proposed model can complete 100 epoches training in 208 seconds when the training set only occupies 20% of the target domain. And achieve a classification accuracy rate about 86%. This is 4% higher than the model without pre-training, and the training time is only one third of the model without pre-training. © 2021 IEEE.

关键词： Cryptography

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