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The various graphs in graph computing 1

引用

22nd Annual Conference on Computer engineering and Technology, NCCET 2018

作者： Sun, Rujun Zhang, Lufei State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

ISBN: (数字)9789811359194

ISBN: (纸本)9789811359187

The world is full of relationships, and graph is the most evident representation for them. With the increasing of data scale, graphs become larger and have encountered a new world of analyzing. What can we learn from a graph? How many kinds of graphs are there? How different is graph from one area to that from another? All these questions need answers, but previous research on graph computing mainly focused on computing frameworks and systems, paying little attention to graph itself. In this paper, we studied graphs of different kinds, different scales and different mining methods, trying to give a sketcher and classification of graph categories. Besides, we studied characters and analyzed algorithms in each category. We researched public graph datasets to show current graph scale and its trend for future infrastructure. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Graphic methods

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PFCA: An Intelligent Method of Multidimensional User Profile Construction and Application 7

PFCA: An Intelligent Method of Multidimensional User Profile...

引用

7th International Conference on Big Data Analytics, ICBDA 2022

作者： Xia, Yi Zhou, Gang Luo, Junyong Lan, Mingjing Chen, Xiaohui Wang, Shiyu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665479387

User profile plays an important role in analyzing high influencers in the era of big data. For example, the profile can help the political strategists to scrutinize the performance of a candidate or party and overcome their weakness long before election day. In this work, we propose a novel profile construction and application model (PFCA) that aims to discover the overall character of high influencers, and apply PFCA to Donald Trump, one of the most influential people recently, to identify Trump's comprehensive personalities. We first collect and preprocess profile data from static and dynamic aspects of multi-source data, and then categorize multi-source data topics and emotions. Next, we attempt to construct the profile from four aspects: fundamental characteristic, behavior characteristic, value characteristic and situation characteristic, to explore the correlation among these characteristics. Finally, PFCA is verified by the outcomes of the 2016 and 2020 U.S. presidential elections, and is evaluated by relevant experts, which proves that PFCA is relatively effective and efficient to profile high influencers. Moreover, PFCA can be further applied to related analysis and prediction in the 2024 U.S. presidential election. © 2022 IEEE.

关键词： Sentiment analysis

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Zero WFuzzer: Target-Oriented Fuzzing for Web Interface of Embedded Devices 4

Zero WFuzzer: Target-Oriented Fuzzing for Web Interface of E...

引用

4th IEEE International Conference on Computer and Communication engineering Technology, CCET 2021

作者： Zheng, Yan Dong, Weiyu Cheng, Hui State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665438902

With the popularization of embedded devices, security issues continue to occur. The existing automated security testing methods for embedded devices are mainly carried out from two aspects: the Web interface of the device or the firmware binary file. This paper proposes a testing framework ZeroWFuzzer that combines the Web interface of embedded devices with firmware binary, which automatically generates test cases through the Web interface, and uses binary instrumentation to guide the fuzzing test. Experiments in embedded devices have found that ZeroWFuzzer has higher efficiency and quantity of triggering vulnerabilities than the protocol fuzzing tool Boofuzz and the Web security testing tool Xray. © 2021 IEEE.

关键词： Firmware

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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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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 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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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 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 Malware Classification Method based on Attentive Bidirectional Model 7

A Malware Classification Method based on Attentive Bidirecti...

引用

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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