Academic literature on the topic 'Network-hardening'

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Journal articles on the topic "Network-hardening"

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Zhao, Chao, Huiqiang Wang, Junyu Lin, Hongwu Lv, and Yushu Zhang. "A Generation Method of Network Security Hardening Strategy Based on Attack Graphs." International Journal of Web Services Research 12, no. 1 (2015): 45–61. http://dx.doi.org/10.4018/ijwsr.2015010104.

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Analyzing attack graphs can provide network security hardening strategies for administrators. Concerning the problems of high time complexity and costly hardening strategies in previous methods, a method for generating low cost network security hardening strategies is proposed based on attack graphs. The authors' method assesses risks of attack paths according to path length and the common vulnerability scoring system, limits search scope with a threshold to reduce the time complexity, and lowers cost of hardening strategies by using a heuristic algorithm. The experimental results show that th
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Pan, Zhong Feng, Gui Cheng Wang, Chong Lue Hua, and Hong Jie Pei. "Research and Development of LM Neural Network Prediction System for Grind-Hardening." Key Engineering Materials 416 (September 2009): 248–52. http://dx.doi.org/10.4028/www.scientific.net/kem.416.248.

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An improved neural network based on L-M algorithm has been applied to the prediction of the grind-hardening parameters against to the slow convergence rate of conventional BP neural network. And the the neural network model for grind-hardening is established. The neural network prediction system for grind-hardening process has been developed based on L-M algorithm. The functions of system is analyzed, particularly and some pivotal technology to realize the system are put forward.
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Manzanares, Antonio Izquierdo. "Hardening Network Infrastructure: Not Suitable for Everyone." IEEE Distributed Systems Online 8, no. 10 (2007): 4. http://dx.doi.org/10.1109/mdso.2007.4384584.

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Wang, Lingyu, Steven Noel, and Sushil Jajodia. "Minimum-cost network hardening using attack graphs." Computer Communications 29, no. 18 (2006): 3812–24. http://dx.doi.org/10.1016/j.comcom.2006.06.018.

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Borbor, Daniel, Lingyu Wang, Sushil Jajodia, and Anoop Singhal. "Surviving unpatchable vulnerabilities through heterogeneous network hardening options." Journal of Computer Security 26, no. 6 (2018): 761–89. http://dx.doi.org/10.3233/jcs-171106.

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Myung, David, Wongun Koh, Jungmin Ko, et al. "Biomimetic strain hardening in interpenetrating polymer network hydrogels." Polymer 48, no. 18 (2007): 5376–87. http://dx.doi.org/10.1016/j.polymer.2007.06.070.

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Wu, Xiao Ling, and Fei Ren. "Research on the Prediction Model of Laser Surface Hardening Index on Cylinder Liner Based on RBF." Advanced Materials Research 148-149 (October 2010): 215–18. http://dx.doi.org/10.4028/www.scientific.net/amr.148-149.215.

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Based on the data obtained from the perpendicular experiment of laser surface hardening on HT150 cylinder liner of a certain motor engine, a Radial Basis Function (RBF) neural network prediction model of laser surface hardening index about HT150 cylinder liner is established by Matlab neural network toolbox in this paper. The parameters of input layer are affirmed by analyzing influential factors of the hardened index ,and the best form of the network is affirmed by selecting suitable spread in function-newrb( ),and as a result, the prediction accuracy and the adaptability of the network are i
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Yang, Tung Sheng, and Huai Shiun Lu. "Predictions of Springback of Strain-Hardening Material in U-Shaped Bending Process." Key Engineering Materials 419-420 (October 2009): 481–84. http://dx.doi.org/10.4028/www.scientific.net/kem.419-420.481.

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This study applies the finite element method (FEM) in conjunction with an abductive network to predict springback of different strain-hardening material in U-shaped bending process.Springback is investigated for different material parameters, such as strength coefficient of material, strain-hardening exponent and Young’s modulus, by finite element analysis during U-shaped bending process. The abductive network is then applied to synthesize the data sets obtained from the numerical simulations. Prediction results of the springback of different strain-hardening material in U-shaped bending proce
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Zhang, Hong, Zhaoguang Ma, Da Kang, and Min Yang. "A Beam Hardening Artifact Correction Method for CT Images Based on VGG Feature Extraction Networks." Sensors 25, no. 7 (2025): 2088. https://doi.org/10.3390/s25072088.

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In X-ray industrial computed tomography (ICT) imaging, beam hardening artifacts significantly degrade the quality of reconstructed images, leading to cupping effects, ring artifacts, and reduced contrast resolution. These issues are particularly severe in high-density and irregularly shaped aerospace components, where accurate defect detection is critical. To mitigate beam hardening artifacts, this paper proposes a correction method based on the VGG16 feature extraction network. Continuous convolutional layers automatically extract relevant features of beam hardening artifacts, establish a non
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Liang, Ruijun, Zhiqiang Wang, Shuying Yang, and Weifang Chen. "Study on hardness prediction and parameter optimization for carburizing and quenching: an approach based on FEM, ANN and GA." Materials Research Express 8, no. 11 (2021): 116501. http://dx.doi.org/10.1088/2053-1591/ac3279.

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Abstract A proper hardening depth is critical to the load-bearing capacity of a part, and heat treatment, including carburizing and quenching, can highly determine the hardness distribution in the part’s surface after manufacturing. This paper proposes a ‘hardness prediction and parameter optimization’ approach that deploys the finite element method (FEM), the artificial neural network (ANN), and the Genetic Algorithm (GA), to describe the relationships between the carburizing/quenching parameters and the hardening depths and conversely to determine the optimized parameters for a given hardeni
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Dissertations / Theses on the topic "Network-hardening"

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Pekkanen, Peter, and David Söderman. "Network Hardening of Multilayer Switch." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-54674.

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In today’s computer networking and industrial networking, security holds a significant role in keeping the entire network safe from malicious users or attackers from remote locations. Using vulnerability scanners and a port scanner it is possible to detect vulnerabilities on the network equipment before an attacker finds the vulnerability and exploits it. In this thesis network hardening has been conducted through a case study on a multilayer switch, with the intent to discover vulnerabilities related to gaining unauthorized access to the device during operation. The tools included in this pro
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Elwing-Malmfelt, Linus, and Oscar Keresztes. "Semi-automated hardening of networks based on security classifications." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21793.

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Conducting risk assessments is a vital part of securing information systems. The task of conducting risk assessments is a time-consuming and costly task for organizations. Thus different security control frameworks have been created to assist in the process. These security control frameworks consists of information about what the organization is supposed to implement to achieve a level of security in their information system. To understand what network hardening solution to use and in what part of the system, an analyst needs to manually use the implementation details gathered from the framewo
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Simons, William R. "The challenges of network security remediation at a regional university." [Johnson City, Tenn. : East Tennessee State University], 2005. http://etd-submit.etsu.edu/etd/theses/available/etd-0209105-075524/unrestricted/SimonsW022105f.pdf.

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Thesis (M.S.)--East Tennessee State University, 2005.<br>Title from electronic submission form. ETSU ETD database URN: etd-0209105-075524 Includes bibliographical references. Also available via Internet at the UMI web site.
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Yildiz, Ersan. "Lateral Pressures On Rigid Retaining Walls : A Neural Network Approach." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1264415/index.pdf.

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Lateral pressures on non-yielding walls due to surface strip loads were investigated considering the non-linear stress-strain behaviour of the soil by finite element analyses. Data obtained from the finite element analyses were used to train neural networks in order to obtain a solution to assess the total lateral thrust and its point of application on a non-yielding wall due to a strip load. A 2-layered backpropogation type neural network was used. An artificial neural network solution was obtained, as a function of six parameters including the shear strength parameters of the soil ( cohesion
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Islam, Tania. "A heuristic approach to network hardening using attack graphs." Thesis, 2009. http://spectrum.library.concordia.ca/976518/1/MR63037.pdf.

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In defending against multi-step attacks, network hardening answers the following important question: Which vulnerabilities must be removed from a network in order to prevent attackers from compromising critical resources while minimizing the implied cost in terms of availability or administrative efforts. Existing approaches to network hardening derive a logic proposition to represent the negation of the attack goal in terms of initially satisfied security conditions. In the disjunctive normal form (DNF) of the logic proposition, each disjunction then provides a viable solution to network hard
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Books on the topic "Network-hardening"

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Wang, Lingyu, Massimiliano Albanese, and Sushil Jajodia. Network Hardening. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04612-9.

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John, Mallery, ed. Hardening network security. McGraw-Hill/Osborne, 2005.

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Albanese, Massimiliano (Computer scientist), author and Jajodia Sushil author, eds. Network hardening: An automated approach to improving network security. Springer, 2014.

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Wang, Lingyu, Sushil Jajodia, and Massimiliano Albanese. Network Hardening: An Automated Approach to Improving Network Security. Springer London, Limited, 2014.

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Security Sage's Guide to Hardening the Network Infrastructure. Elsevier, 2004. http://dx.doi.org/10.1016/b978-1-931836-01-2.x5000-3.

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Kenyon, Brian. Security Sage's Guide to Hardening the Network Infrastructure. Syngress, 2004.

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Kenyon, Brian, Steven Andres, and Erik Pack Birkholz. Security Sage's Guide to Hardening the Network Infrastructure. Elsevier Science & Technology Books, 2004.

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Hardening network infrastructure: [bulletproof your systems before you are hacked!]. McGraw-Hill/Osborne, 2004.

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Book chapters on the topic "Network-hardening"

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Wang, Lingyu, Massimiliano Albanese, and Sushil Jajodia. "Introduction." In Network Hardening. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04612-9_1.

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Wang, Lingyu, Massimiliano Albanese, and Sushil Jajodia. "Related Work." In Network Hardening. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04612-9_2.

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Wang, Lingyu, Massimiliano Albanese, and Sushil Jajodia. "Attack Graph and Network Hardening." In Network Hardening. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04612-9_3.

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Wang, Lingyu, Massimiliano Albanese, and Sushil Jajodia. "Minimum-Cost Network Hardening." In Network Hardening. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04612-9_4.

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Wang, Lingyu, Massimiliano Albanese, and Sushil Jajodia. "Linear-Time Network Hardening." In Network Hardening. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04612-9_5.

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Wang, Lingyu, Massimiliano Albanese, and Sushil Jajodia. "Conclusion." In Network Hardening. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04612-9_6.

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Wang, Lingyu. "Network Hardening." In Encyclopedia of Cryptography, Security and Privacy. Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-642-27739-9_1776-1.

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Wang, Lingyu. "Network Hardening." In Encyclopedia of Cryptography, Security and Privacy. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-030-71522-9_1776.

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Camp, L. Jean. "Hardening the Network from the Friend Within." In Information Security and Privacy. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02620-1_17.

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Bopche, Ghanshyam S., and Babu M. Mehtre. "Exploiting Domination in Attack Graph for Enterprise Network Hardening." In Communications in Computer and Information Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22915-7_32.

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Conference papers on the topic "Network-hardening"

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Rausch, Thiago H., Wesley Grignani, Gustavo H. S. Müller, Douglas A. Santos, Luigi Dilillo, and Douglas R. Melo. "Hardening an AMBA-AXI Network Interface for a Reliable Network-on-Chip." In 2025 IEEE 16th Latin America Symposium on Circuits and Systems (LASCAS). IEEE, 2025. https://doi.org/10.1109/lascas64004.2025.10966348.

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Tatebatake, Gaku, and Shingo Yamaguchi. "Network Hardening Method by Small-Scale Reconfiguration with Vulnerability Consideration." In 2024 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia). IEEE, 2024. https://doi.org/10.1109/icce-asia63397.2024.10773914.

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Shur, D., G. Di Crescenzo, T. Chen, et al. "Energy-efficient Hardening of the SEDIMENT Methodology for Scalable IoT Network Security." In 2024 IEEE International Conference on Cyber Security and Resilience (CSR). IEEE, 2024. http://dx.doi.org/10.1109/csr61664.2024.10679493.

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Guillemé, Wilfread, Youri Helen, Rémy Priem, Angeliki Kritikakou, Daniel Chillet, and Cédric Killian. "Hardening a Neural Network on FPGA through Selective Triplication and Training Optimization." In 2023 23rd European Conference on Radiation and Its Effects on Components and Systems (RADECS). IEEE, 2023. https://doi.org/10.1109/radecs59069.2023.10767059.

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Beyer, Michael, Christoph Schorn, Tagir Fabarisov, Andrey Morozov, and Klaus Janschek. "Automated Hardening of Deep Neural Network Architectures." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-72891.

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Abstract Designing optimal neural network (NN) architectures is a difficult and time-consuming task, especially when error resiliency and hardware efficiency are considered simultaneously. In our paper, we extend neural architecture search (NAS) to also optimize a NN’s error resilience and hardware related metrics in addition to classification accuarcy. To this end, we consider the error sensitivity of a NN on the architecture-level during NAS and additionally incorporate checksums into the network as an external error detection mechanism. With an additional computational overhead as low as 17
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Jun-chun, Ma, and Sun Ji-yin. "Optimal Network Hardening Model Based on Parallel Genetic Algorithm." In 2012 International Conference on Industrial Control and Electronics Engineering (ICICEE). IEEE, 2012. http://dx.doi.org/10.1109/icicee.2012.150.

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Gang, Liu, Ma Yingge, Wang Kejun, and Huang Shaobin. "A Domain Security Ontology Network Constructing and Hardening Technology." In 2014 Fourth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC). IEEE, 2014. http://dx.doi.org/10.1109/imccc.2014.167.

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Yuan, Zijun, Heng Zhang, Lu Liu, et al. "Resilient Transmission Network Hardening Planning Coordinated with Distribution Network Defensive Strategies Against Typhoons." In 2023 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia). IEEE, 2023. http://dx.doi.org/10.1109/icpsasia58343.2023.10294960.

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Albanese, Massimiliano, Sushil Jajodia, and Steven Noel. "Time-efficient and cost-effective network hardening using attack graphs." In 2012 42nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE, 2012. http://dx.doi.org/10.1109/dsn.2012.6263942.

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Priambodo, Dimas Febriyan, Amiruddin, and Nanang Trianto. "Hardening a Work from Home Network with Wireguard and Suricata." In 2021 International Conference on Computer Science and Engineering (IC2SE). IEEE, 2021. http://dx.doi.org/10.1109/ic2se52832.2021.9791983.

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