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NIG-AP: a new method for automated penetration testing

Frontiers of Information Technology & Electronic engineering 2019年第9期20卷 1277-1288页

作者： Tian-yang ZHOU Yi-chao ZANG Jun-hu ZHU Qing-xian WANG State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450001China China National Digital Switching System Engineering and Technological R&D Center Zhengzhou 450001China

Penetration testing offers strong advantages in the discovery of hidden vulnerabilities in a network and assessing network ***,it can be carried out by only security analysts,which costs considerable time and *** natural way to deal with the above problem is automated penetration testing,the essential part of which is automated attack *** previous studies have explored various ways to discover attack paths,all of them require perfect network information beforehand,which is contradictory to realistic penetration testing *** vividly mimic intruders to find all possible attack paths hidden in a network from the perspective of hackers,we propose a network information gain based automated attack planning(NIG-AP)algorithm to achieve autonomous attack path *** algorithm formalizes penetration testing as a Markov decision process and uses network information to obtain the reward,which guides an agent to choose the best response actions to discover hidden attack paths from the intruder’s *** results reveal that the proposed algorithm demonstrates substantial improvement in training time and effectiveness when mining attack paths.

关键词： Penetration testing Reinforcement learning Classical planning Partially observable Markov decision process

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Implementation of Multi-compartment Neuron Model with Synaptic Plasticity Using FPGA 21

Implementation of Multi-compartment Neuron Model with Synapt...

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21st IEEE International Conference on Communication Technology, ICCT 2021

作者： DIng, Yiwen Fu, Wenlong Cao, Lihong Communication University of China State Key Laboratory of Media Convergence and Communication Beijing China Neuroscience and Intelligent Media Institute Communication University of China Beijing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

ISBN: (纸本)9781665432061

Computational neuroscience is interdisciplinary and plays an essential role in facilitating the development of cognitive neuroscience and Artificial General Intelligence (AGI). To explore the computational efficiency of Spiking Neural Network (SNN) on a computational hardware platform, we deploy SNN on the Xilinx Zynq7 Zedboard Field Programmable Gate Array (FPGA). Specifically, we use multi-compartmental Hodgkin-Huxley (HH) neuron model to construct neural circuits via C++ code and adopt a biologically plausible calcium concentration-based plasticity model as the learning rule of SNN. Our experimental results show that the FPGA platform can support parallel computation of the multi-compartment HH model with synaptic plasticity. Compared to other methods, FPGA-based computing has a significant speed advantage, suggesting that FPGA can be a good choice for implementing brain-inspired computation. © 2021 IEEE.

关键词： Field programmable gate arrays (FPGA)

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SoK: A Survey on Redundant Execution Technology

SoK: A Survey on Redundant Execution Technology

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advanced computing and Endogenous Security, International Conference on

作者： Zijing Liu Zheng Zhang Ruicheng Xi Pengzhe Zhu Bolin Ma State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665424509

Redundant execution technology is one of the effective ways to improve the safety and reliability of computer systems. By rationally configuring redundant resources, adding components with the same function, using the determined redundant execution logic to coordinate and efficiently execute synchronously can effectively ensure high availability of the machine and system. Fault-tolerant is based on redundant execution, which is the primary method of dealing with system hardware failures. Recently, multi-threading redundancy has realized the continuous development of fault-tolerant technology, which makes the processing granularity of the system tolerate random failure factors gradually reduced. At the same time, intrusion tolerant technology has also been continuously developed with the emergence of multi-variant execution technology. It mainly uses the idea of dynamic heterogeneous redundancy to construct a set of variants with equivalent functions and different structures to complete the detection and processing of threats outside the system. We summarize the critical technologies of redundant execution to achieve fault tolerance and intrusion tolerance in recent years, sorts out the role of redundant execution in the development process from fault tolerance technology to intrusion tolerance technology, classify redundant execution technologies at different levels, finally point out the development prospects of redundant execution technology in multiple application fields and future technical research directions.

关键词： Fault tolerant systems Redundancy Cyberspace Games Computer architecture Hardware Safety

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Secure and efficient smart card based remote user password authentication scheme

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International Journal of Network Security 2016年第4期18卷 782-792页

作者： Wei, Jianghong Liu, Wenfen Hu, Xuexian State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan Province450002 China

In distributed systems, the smart card based password authentication, as one of the most convenient and efficienttwo-factor authentication mechanisms, is widely used toensure that the protected services are not available to unauthorized users. Recently, Li et al. demonstrated that the smart card ased password authentication schemeproposed by Chen et al. cannot provide perfect forward secrecy as they claimed. In addition, the password change phase of the scheme is unfriendly and inefficient. Subsequently, Li et al. presented an enhanced smart card based password authentication scheme to overcome the above aws existing in Chen et al.'s scheme. Further more, Kumari and Khan, and Jiang et al. demonstrated that Chen et al.'s scheme cannot resist off-line password guessing attacks, and also proposed an improved scheme, respectively. In this study, we first illustrate that Li et al.'s scheme, and Kumari and Khan's scheme both failto achieve the basicsecurity requirement of the smartcard based password authentication, namely, once the private information stored in the smart card has been extracted, the schemes would be vulnerable to off-line password guessing attacks. We also point out that Jiang etal.'s scheme, as well as Kumari and Khan's scheme cannot provide perfect forward secrecy. Then, we introducea new smart card based password authentication scheme. By presenting concrete analysis of security and performance, we show that the proposed scheme cannot onlyresist various wellknown attacks, but also is more efficient than other related works, and thus is feasible forpractical applications.

关键词： Smart cards

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Research on 3D Human Body Reconstruction Algorithm of Monocular Image 5

Research on 3D Human Body Reconstruction Algorithm of Monocu...

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5th Asian Conference on Artificial Intelligence Technology, ACAIT 2021

作者： Liang, Zijian Liang, Huan Fu, Wenlong Neuroscience and Intelligent Media Institute Communication University of China Beijing China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

ISBN: (纸本)9781665426305

The three-dimensional human body feature parameters extracted by the neural network method are coupled in the high-dimensional feature space. The fully connected network processes the three-dimensional human pose and shape feature information synchronously, which cannot handle multifactor features well, which makes the model perform poorly. To solve this problem, on the basis of the benchmark network, a decoupling model is added, which processes the three-dimensional human pose and shape information separately, and finally integrates the features through the bi-linear transformation method. Through experimental verification, the algorithm effectively reduces the reconstruction error, and the improved algorithm performs better. © 2021 IEEE.

关键词： Three dimensional computer graphics

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Embedding change rate estimation based on ensemble learning 13

Embedding change rate estimation based on ensemble learning

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1st ACM Workshop on Information Hiding and Multimedia Security, IH and MMSec 2013

作者： Li, Zhenyu Hu, Zongyun Luo, Xiangyang Lu, Bin State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450002 China Zhengzhou Institute of Information Science and Technology Zhengzhou 450002 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing 100093 China

ISBN: (纸本)9781450320818

In order to achieve higher estimation accuracy of the embedding change rate of a stego object, an ensemble learning-based estimation method is presented. First of all, a framework of embedding change rate estimation based on estimator ensemble is proposed. Then an algorithm of building the estimator ensemble, the core of the framework, is concretely described. Finally, a pruning method for estimator ensemble is proposed in consideration of both the diversity among the base estimators and accuracy of each of them. The experimental results for three modern steganographic algorithms (nsF5, PQ and PQt) indicate that the proposed method acquired better performance than the existed typical method. Furthermore, the pruned estimator ensemble with less base estimators maintained, even slightly improved the estimation accuracy, compared to the one without purning. © 2013 ACM.

关键词： Steganography

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An algorithm/hardware co-optimized method to accelerate CNNs with compressed convolutional weights on FPGA

An algorithm/hardware co-optimized method to accelerate CNNs...

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作者： Shang, Jiangwei Zhang, Zhan Zhang, Kun Li, Chuanyou Qian, Lei Liu, Hongwei School of Computer Science and Technology Harbin Institute of Technology Harbin China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China School of Computer Science and Engineering Southeast University Nanjing China

Convolutional neural networks (CNNs) have shown remarkable advantages in a wide range of domains at the expense of huge parameters and computations. Modern CNNs still tend to be more complex and larger to achieve better inference accuracy. However, the complex and large structures of CNNs could slow down the inference speed. Recently, Compressing the convolutional weights to be sparse by pruning the unimportant parameters has been demonstrated as an efficient way to reduce the computations of CNNs. On the other hand, field-programmable gate arrays (FPGAs) have been a popular hardware platform to accelerate CNN inference. In this paper, we propose an algorithm/hardware co-optimized method for accelerating CNN inference on FPGAs. For the algorithm, we take advantage of unstructured and structured parameter sparsifying methods to achieve high sparsity and keep the regularity of convolutional weights. Correspondingly, hardware-friendly index representations of sparse convolutional weights are proposed. For the hardware architecture, we propose row-wise input-stationary dataflow, which is tightly coupled with the algorithm. A row-wise computing engine (RConv Engine) is proposed, which is based on the dataflow. Inside the RConv Engine, the scalar-vector structure is applied to implement the basic processing elements (PEs). To flexibly calculate the feature map with various sizes, the PEs are organized in a 2D structure with two work modes. The experimental results demonstrate that our co-optimized method implements high sparsity of convolutional weights, and the computing engine achieves high computation efficiency. Compared with other accelerators, our co-optimized method implements a 10.9 (Formula presented.) speedup on FPS at most with the highest sparsity of convolutional weights and negligible accuracy loss. © 2024 John Wiley & Sons Ltd.

关键词： Engines

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A software trustworthiness measure based on the decompositions of trustworthy attributes and its validation

A software trustworthiness measure based on the decompositio...

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International Conference on Industrial engineering, Management Science and Applications, ICIMSA 2015

作者： Tao, Hongwei Chen, Yixiang Pang, Jianmin State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou45002 China MoE Engineering Center for Software/Hardware Co-Design Technology and Application East China Normal University Shanghai200062 China

ISBN: (纸本)9783662471999

There exist many software trustworthiness measures based on the decompositions of attributes. However, few of them are concerned with using more rigorous approaches to measure software trustworthiness and carrying out the theoretical validation of measures. Axiomatic approaches formalize the empirical understanding of a software attribute through the definition of a set of desirable software measure properties. It can provide precise and formal terms for the quantification of software attributes. We once used axiomatic approaches to measure software trustworthiness based on multi-dimensional attributes, in this paper, based on previous works, we apply axiomatic approaches to measure software trustworthiness based on the decompositions of attributes, propose the desirable properties of software trustworthiness measures based on the decompositions of attributes, present a software trustworthiness measure and validate this measure from the theory by proving that it complies with the set of properties. Compared with the popular software trustworthiness measures, this measure can evaluate software trustworthiness better in view of our properties. © Springer-Verlag Berlin Heidelberg 2015.

关键词： Computers

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Network Topology Overlapping Group Detection Based on h-Core Pruning 6

Network Topology Overlapping Group Detection Based on h-Core...

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6th IEEE International Conference on Intelligent computing and Signal Processing, ICSP 2021

作者： Zhang, Qi Liu, Yan Cai, Yuheng Zhong Yuan Network Security Research Institute Zhengzhou University Zhengzhou China Pla Strategic Support Force Information Engineering University State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9780738143705

The network group detection has a large number of application researches and has achieved certain results in network security, IP entity location and load balancing. However, due to the large scale of the network topology graph and the large number of terminal nodes, most of the existing overlapping group detection algorithms are relatively complex, and the data size that can be processed is limited, and cannot be directly applied to the network topology. This paper proposes a network topology overlapping group detection algorithm based on h-core pruning. This algorithm decomposes the network into smaller strong connected components, reduces the interference of noisy nodes to overlapping group discovery, and more efficiently detect the largest k-plex group on the target network. A large number of experiments show that the method in this paper is effective. Compared with the full-enum algorithm, our method increases the network size that can be handled by the largest k-plex group by two orders of magnitude, and can detect overlapping groups in the network topology more efficiently. © 2021 IEEE.

关键词： Network topology

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Social User Geolocation Method Based on POI Location Feature Enhancement in Context 1

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30th International Conference on Computational and Experimental engineering and Sciences, ICCES 2024

作者： Liu, Yu Qiao, Yaqiong Liu, Yimin Du, Shaoyong State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Key Laboratory of Cyberspace Situation Awareness of Henan Province Zhengzhou China School of Information Engineering North China University of Water Resources and Electric Power Zhengzhou China

ISBN: (数字)9783031687754

ISBN: (纸本)9783031687747

The geolocation information in social media user generated text is one of the important bases for inferring users' location. The existing methods often rely on the mentioned Point of Interest (POI) and location Indicative word in user generated text as the inference basis, in consideration of the lack of consideration of semantic information and the inaccuracy or incompleteness of POI contribute to the inaccuracy of user geolocation based on text. To address these issues, this paper proposes Social User Geolocation Method based on POI Location Feature Enhancement in Context (UGLE). Firstly, the POI in user-generated text is removed by deep neural network. Then, through three geographical traits of POI: location orientation, word frequency and word length, the problem of ambiguous POI location orientation in user generated text is solved. Finally, by combining the semantic information of the words around POI, the user vector with enhanced location features is generated, which improves the geolocation accuracy of social users. On the Weibo dataset, the proposed Method is compared with four existing typical methods, PaQL, SeFG, MNB-PART, and MNB-LGR. The results show that the proposed method's accuracy in inferring province-level and city-level locations is 76.78% and 62.56%, respectively. These results are 6.81% and 6.66% higher than the existing best results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep neural networks

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