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A survey of industrial control system testbeds 2

A survey of industrial control system testbeds

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2019 2nd International Conference on advanced Materials, Intelligent Manufacturing and Automation, AMIMA 2019

作者： Geng, Yangyang Wang, Yi Liu, Wenwen Wei, Qiang Liu, Ke Wu, Haolan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China Department of Research and Development Center Beijing Sifang Automation Co. Ltd Beijing100085 China

Industrial Control System(ICS) testbed is the basis of ICS safety research. In order to build an ICS testbed, it is necessary to have a deep understanding of the current research status. This paper introduced the structure and composition of the typical ICS system. Then it simply analyzed the technical characteristics, system framework and advantages and disadvantages of four types of typical ICS testbeds. Finally, it summarized application scenarios of ICS testbed and pointed out some problems in testbed construction and the development direction of ICS testbed with the fusion of cyber and physical. © Published under licence by IOP Publishing Ltd.

关键词： Testbeds

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SIMD vectorization of nested loop based on strip mining

SIMD vectorization of nested loop based on strip mining

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ACIS International Conference on Software engineering, Artificial Intelligence, Networking, and Parallel/Distributed computing (SNPD)

作者： Jinlong Xu Huihui Sun Rongcai Zhao State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

The difference between vector machine and SIMD extension is analyzed at the very start. The multilevel loop vector code generation algorithm termed Codegen put forward by Kennedy and other fellows can't be directly applied to SIMD extension as it is oriented to vector machine. The vectorization algorithm in state-of-the-art compilers can only process one level of nested loop. In order to vectorize the entire nested loop, a vectorization algorithm based on strip mining called simdcodegen is proposed. Firstly, the formation reason of dependence circles is analyzed and the role of strip mining played in elimination of dependence circles is discussed. Then on the basis of codegen, strip mining is applied on each level of the loop recursively to explore the SIMD parallelism in the nested loop. Although strip mining is always legitimate, the executing cost increases after strip mining. To assure that strip mining is beneficial, that is to say, strip mining can break some dependence circles, cycle broken test is applied before implementation of strip mining. Effectiveness of this method is verified by the experimental results.

关键词： Strips Support vector machines Parallel processing Data mining Fellows Registers Data processing

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Clustering and Routing Optimization Algorithm for Heterogeneous Wireless Sensor Networks

Clustering and Routing Optimization Algorithm for Heterogene...

引用

International Wireless Communications and Mobile computing Conference, IWCMC

作者： Ling Chen Wenwen Liu Daofu Gong Yan Chen State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781728131290

ISBN: (纸本)9781728131306

Wireless sensor network (WSN) is the cutting-edge technology of modern monitoring technology. There is a direct relationship between the length of its lifetime and its performance. Efficient and reasonable clustering and routing algorithm can reduce the energy loss in the network, which is of great significance for prolonging the network lifetime. The traditional WSN protocol has been difficult to meet the needs of the contemporary society. This paper focuses on the advantages and disadvantages of the traditional WSN protocol, and puts forward relevant improvement measures to optimize the traditional WSN protocol and extend the network life time.

关键词： Wireless sensor networks Clustering algorithms Routing Energy consumption Head Wireless communication Clustering methods

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A malware detection system based on intermediate language

A malware detection system based on intermediate language

引用

International Conference on Systems and Informatics (ICSAI)

作者： Binglin Zhao Jin Han Xi Meng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Malware remains a serious problem for economic corporations, government agencies, and individuals. With the evolution of malware, the malware detection method based on signature, reputation or anomaly show the lack of detection ability. In this paper, we proposed a novel malware detection system, which use the feature extract from intermediate representation and utilizes multiple evaluation methods to detect the malware. We extract the feature from intermediate representation with fine-grained that retain the functional sequence of malware. Then, we use LSTM (Long Sort Term Memory) to learn the pattern from the training set and build a detection system that contain many antivirus software's results to detect malware and classify it. Our approach is evaluated on more than 300,000 samples. Experimental results show that our system can effectively detect unknown malware and classify it.

关键词： Malware Feature extraction Training Classification algorithms Flow graphs Androids

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Binary software vulnerability detection method based on attention mechanism

Binary software vulnerability detection method based on atte...

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International Conference on Mechanical, Control and Computer engineering (ICMCCE)

作者： Wenjie Han Jianmin Pang Xin Zhou Di Zhu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Aiming at the stack overflow vulnerability in binary software, this paper proposes a binary vulnerability detection method based on the attention mechanism. First, this paper analyze the basic characteristics of stack overflow vulnerabilities, and perform data preprocessing on the decompiled files to make the neural network better adapt to the characteristics of stack overflow vulnerabilities, then formulate instruction specifications at the assembly language level, and finally input the data into the fusion attention mechanism Learning in the neural network. This paper compares and analyzes three kinds of neural networks on the CWE121 data set. The experimental results show that after neural network training, the detection method based on the attention mechanism can be effective and accurately discover whether the target area has stack overflow vulnerabilities, thereby greatly improving the detection efficiency.

关键词： Training Computational modeling Neural networks Force Data preprocessing Software Data models

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Field-aware Evolutionary Fuzzing Based on Input Specifications and Vulnerability Metrics

Field-aware Evolutionary Fuzzing Based on Input Specificatio...

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IEEE International Conference on Software engineering and Service Sciences (ICSESS)

作者： Yunchao Wang Zehui Wu Qiang Wei Qingxian Wang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781728109459

ISBN: (纸本)9781728109466

Evolutionary fuzzing technology based on genetic algorithm has become one of the most effective vulnerability discovery techniques due to its fast and scalable advantages. How to effectively mutate the seed input plays a crucial role in improving the efficiency of the fuzzing. A good mutation strategy can increase code coverage and vulnerability triggering probability. Existing fuzzing tools generally focus on how to mutate smartly to improve code coverage to find more vulnerabilities (such as passing the branch with magic bytes), but they still face two challenges which substantially reduces the efficiency of vulnerability discovery. First, the input space is huge and current fuzzers are not aware of the input format, resulting in many mutated inputs are invalid. Second, they believe all bytes are equal and mutate them sequentially, wasting lots of time testing some uninteresting bytes. To this end, this paper proposes a field-aware mutation strategy that can find more vulnerabilities by generating fewer but more effective inputs. Specifically, we extract the field and type information of the seed input through the existing input specifications to ensure that the mutation is performed in field level instead of byte level and the optimal mutation strategy is selected. At the same time, the input fields are scored by code assessment based on vulnerability metrics, thus the more important fields (i.e., fields that are more likely to trigger the vulnerability) are prioritized to be mutated. We implemented a prototype tool, FaFuzzer, and evaluated it on two different datasets consisting of a variety of real-world applications. Experiments show that our field-aware strategy can find more vulnerabilities with fewer inputs than existing tools, while maintaining high code coverage. We found many unknown bugs in five widely used real-world applications and reported them to the relevant vendors.

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Unknown Traffic Identification Based on Deep Adaptation Networks

Unknown Traffic Identification Based on Deep Adaptation Netw...

引用

IEEE Conference on ALocal Computer Networks Workshops (LCN Workshops)

作者： Ziji Yang Wei Lin State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781728183145

ISBN: (纸本)9781728183152

Network traffic classification has become an important basis for computer networks. However, the emergence of new applications, which generate unknown traffic constantly, has brought new challenges. The most critical challenge is how to divide the mixed unknown traffic into clusters containing only one category. In this paper, we propose a transfer learning approach using Deep Adaptation Network (DAN). This approach utilizes a few labeled samples from known traffic to improve the clustering purity of unknown traffic. We first trained a Convolutional Neural Network (CNN) model on unlabeled dataset with sampled time-series features. Then the model was extended to an adaptation model, co-trained on labeled and unlabeled samples. We evaluated our model using two publicly available datasets, achieving a purity of 98.23%. Our results demonstrate the effectiveness of DAN model in unknown traffic clustering. Moreover, we studied three sampling techniques and five clustering algorithms in our model for better clustering performance.

关键词： Adaptation models Protocols Transfer learning Telecommunication traffic labeling Convolutional neural networks Knowledge transfer

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DSA-NIE: Double Static Attributes Based Node Importance Evaluation of Inter-Domain Routing System

DSA-NIE: Double Static Attributes Based Node Importance Eval...

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International Conference on Computer and Communications (ICCC)

作者： Huihu Zhu Han Qiu Junhu Zhu Qingxian Wang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781728147437

ISBN: (纸本)9781728147444

Assessing the importance of AS nodes is important for the protection of inter-domain routing system security. However, the existing literature on node importance evaluation methods based on static attributes are difficult to accurately characterize nodes. And which based on dynamic attributes have high time complexity. To address these problems, we analyze the relationship between static structural attributes and dynamic attributes after node failure. It is found that the degree can indirectly reflect the impact of the UPDATE packet propagation and the number of routing paths can reflect the impact caused by load redistribution. And then, we build static attribute characterization model of node failure effect. After that an evaluation method DSA-NIE is put forward. Given the two static attributes, DSA-NIE could effectively assessment of the importance of nodes. When compared with SD-KNI algorithm, the time complexity has been reduced to O(lmn). And the experiment result shows that the failure effect of the key nodes assessed by the DSA-NIE is 7.4% higher than the average, and up to 12.9% higher.

关键词： Routing Time complexity Kernel Power system faults Power system protection Security Load modeling

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Cluster-Based Routing Algorithm for WSN Based on Subtractive Clustering

Cluster-Based Routing Algorithm for WSN Based on Subtractive...

引用

International Wireless Communications and Mobile computing Conference, IWCMC

作者： Ling Chen Wenwen Liu Daofu Gong Yan Chen State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781728131290

ISBN: (纸本)9781728131306

The Cluster-based Routing Algorithm for Wireless Sensor Network based on Subtractive Clustering (SCC algorithm) generates nodes of cluster head in dense area of the node by relying on subtractive clustering. The application of this algorithm effectively solves the attribution problem of non-cluster head node in conventional algorithms, and consumption is evenly distributed throughout the network. Through simulation experiments, it can be found that the application of the SCC algorithm can map the distribution of nodes of cluster head reasonably and delay the death time of the first node to prolong the lifetime of network and balance the energy consumption of nodes.

关键词： Clustering algorithms Wireless sensor networks Routing Routing protocols Base stations Electronic mail Energy consumption

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Integrating KNN and Gradient Boosting Decision Tree for Recommendation

Integrating KNN and Gradient Boosting Decision Tree for Reco...

引用

IEEE advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Qiming Du Nan Li Shudan Yang Daozhu Sun Wenfu Liu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

As an efficient information filtering tool, the recommendation system can help users obtain valuable information quickly and efficiently while effectively alleviating the information load problem. However, the classic collaborative filtering algorithm has data sparsity problems in practical applications, which affects the accuracy of recommendation. To solve this problem, this paper took movie recommendation as the research object and proposed a hybrid recommendation algorithm named K-GBDT which combined KNN and gradient boosting decision tree. Firstly, it used KNN to obtain the similar information of the target user and item for preliminary prediction. At the same time, it mined users' basic information features and potential features of users and items from the original data. Then, it tried to adopt XGBoost, LightGBM and CatBoost algorithm which were proposed base the idea of gradient boosting decision tree to build regression model. Finally, the target user's rating for the movie was predicted with the trained model, and the recommendation list was generated based on the predicted results. Compared with some classic recommendation algorithms such as NMF, Slope One, Co-Clustering and etc. Based on MovieLens datasets, results show that the mean absolute error and root mean square error of this algorithm are both low, and its recommendation accuracy is higher.

关键词： Training Distributed databases Prediction algorithms Boosting Feature extraction Motion pictures Data models

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