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

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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...

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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 Malware Classification Method based on Attentive Bidirectional Model 7

A Malware Classification Method based on Attentive Bidirecti...

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7th International Conference on Intelligent computing and Signal Processing, ICSP 2022

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

ISBN: (纸本)9781665478571

Deep learning techniques have achieved significant improvements in malware analysis application, such as malware detection, malware classification and vulnerability mining. Convolutional neural network (CNN) is applied to extract local features of samples, while recurrent neural network (RNN) mainly extracts global features from sequence context. However, the pooling layer in CNN is a nonlinear dimensionality reduction operation, which can cause a loss of correlation feature. The raw RNN or LSTM model cannot perform parallel computing when calculating the feature vectors, limiting the effect of model application. Hence, we propose a novel malware classification model named BGSA (Bidirectional GRU with Self- Attention), which applies bi-direction gated recurrent unit (BiGRU) and self-attention mechanism. To demonstrate that the model can improve the feature extraction capability, a series of comparison experiments have been conducted on the VxHeavon dataset. The results show that the classification of BGSA reaches 92.54% in term of precision, which surpass the performance of method based on LSTM+CNN and BiGRU. © 2022 IEEE.

关键词： Long short-term memory

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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...

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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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Topology-Aware Software Assignment in Networked Systems 2

Topology-Aware Software Assignment in Networked Systems

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2019 2nd International Conference on advanced Algorithms and Control engineering, ICAACE 2019

作者： Wang, Junchao Liu, Hao Liu, Zhenwu Zhang, Zheng Pang, Jianmin State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Cyberspace mimic defense has been proven to be a revolutionary defense technology that "changes the rules of the game" to ensure the security of cyberspace. However, the software diversity inherent in mimic defense technology may increase the difficulty in managing software executable binaries, especially when updating or debugging a software. In this paper, we study the problem of software assignment in a networked system to minimise the number of binaries generated by mimic compilers. Mimic compilers can help to generate functional equivelant software executables which exhibits diverse characteristics such as binary size etc. Theoretically the software assignment problem is equal to the traditional graph coloring problem. To guarantee network severity, we apply a Welsh-Powell-based Software Assignment (WPSA) algorithm to determine the number of binaries needed and the assignment of these binaries to hosts in a network. We conduct experiments on real world network topologies. Experimental results show that our algorithm can effectively reduce the number of binaries needed in networked systems. © Published under licence by IOP Publishing Ltd.

关键词： Network topology

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One wide-sense circuit tree per traffic class based inter-domain multicast

One wide-sense circuit tree per traffic class based inter-do...

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2013 IEEE 8th International Conference on Networking, Architecture and Storage, NAS 2013

作者： Yue, Chen Chaoling, Li Kaixiang, Huang Xiaobo, Zhang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9780769550343

Traditional multicast protocol forms multicast trees rooted at different sources to forward packets. If the multicast sources and receivers are in different domains, these trees will produce a great number of multicast states in the backbone, resulting in poor scalability. Therefore, we propose a one Wide-Sense Circuit Tree per Traffic Class based inter-domain multicast (WSCT-TC), in which a Wide-Sense Circuit Tree (WSCT) is established for a class of multicast traffic. The WSCT is established in the backbone, along which multicast packets are forwarded by label switching. The spec of WSCT can be reconfigured according to the QoS (Quality of Service) requirement of multicast applications, to provide preferable QoS. Simulating experiment shows that WSCT-TC behaves better scalability. © 2013 IEEE.

关键词： Scalability

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Practical Control Flow Integrity using Multi-Variant execution 20

Practical Control Flow Integrity using Multi-Variant executi...

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2020 International Conference on Internet computing for Science and engineering, ICICSE 2020 and the 13th International Conference on advanced Computer Theory and engineering, ICACTE 2020

作者： Yao, Dong Zhang, Zheng Zhang, Gaofei State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781450377348

The popularity of computers and networks brings many conveniences to our daily life but also brings a variety of security threats. The proliferation of attacks and the rapid spread of viruses make security researchers gradually shift their perspective from passive defense to active defense. Among many active defenses, the technique of software multi-variant execution (MVX) framework has been widely concerned. It can detect and defend most known or even 0-day attacks without depending on the feature of specific threats. However, there is a big performance bottleneck in this technique. Control flow integrity (CFI) is another ideal security solution, but it is not widely used in practice because of performance loss and compatibility problems. In this paper, we propose a multi-variant execution framework called MVX-CFI. MVX-CFI improves execution efficiency without losing its original security. MVX-CFI is a dynamic and transparent CFI implementation based on the MVX framework. It can effectively capture the control flow of the target software and find illegal path transfer caused by malicious acts such as attacks. MVX-CFI extends the general MVX with a feedback assisted-detection module, which reduces a lot of repetitive voting work of traditional MVX and improves the performance. It also provides a probability to detect preset back door in a software. © 2020 ACM.

关键词： Network security

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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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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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Recommendation of cyber attack method based on knowledge graph

Recommendation of cyber attack method based on knowledge gra...

引用

2020 International Conference on Computer engineering and Intelligent Control, ICCEIC 2020

作者： Ou, Yunjia Zhou, Tianyang Zhu, Junhu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781728185736

The lack of correlation between the new cyber attack intelligence and the knowledge database makes it difficult for the recommendation system to recommend effective cyber attack knowledge in terms of the new security intelligence. This paper proposes an cyber attack method intelligence recommendation algorithm based on knowledge graph. Firstly, it proposes a cyber attack knowledge graph construction scheme based on four kinds of open security databases. Then, based on the idea of collaborative filtering recommendation, the meta-path is introduced to describe different relations between nodes. At last, the recommendation list could be generated by calculating the correlation score of each path with node vector. Compared with the content-based recommendation method, the effectiveness of this method is proved. © 2020 IEEE.

关键词： Knowledge graph

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